{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contrastive Explanations Method (CEM) applied to Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Contrastive Explanation Method (CEM) can generate black box model explanations in terms of pertinent positives (PP) and pertinent negatives (PN). For PP, it finds what should be minimally and sufficiently present (e.g. important pixels in an image) to justify its classification. PN on the other hand identify what should be minimally and necessarily absent from the explained instance in order to maintain the original prediction.\n",
    "\n",
    "The original paper where the algorithm is based on can be found on [arXiv](https://arxiv.org/abs/1802.07623).\n",
    "\n",
    "This notebook requires the seaborn package for visualization which can be installed via pip:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enable support for the Contrastive Explanation Method, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[tensorflow]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version:  2.2.0\n",
      "Eager execution enabled:  False\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel(40) # suppress deprecation messages\n",
    "tf.compat.v1.disable_v2_behavior() # disable TF2 behaviour as alibi code still relies on TF1 constructs\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_iris\n",
    "from alibi.explainers import CEM\n",
    "\n",
    "print('TF version: ', tf.__version__)\n",
    "print('Eager execution enabled: ', tf.executing_eagerly()) # False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare Iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_iris()\n",
    "feature_names = dataset.feature_names\n",
    "class_names = list(dataset.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.data = (dataset.data - dataset.data.mean(axis=0)) / dataset.data.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 145\n",
    "x_train,y_train = dataset.data[:idx,:], dataset.target[:idx]\n",
    "x_test, y_test = dataset.data[idx+1:,:], dataset.target[idx+1:]\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define and train logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_model():\n",
    "    x_in = Input(shape=(4,))\n",
    "    x_out = Dense(3, activation='softmax')(x_in)\n",
    "    lr = Model(inputs=x_in, outputs=x_out)\n",
    "    lr.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3)                 15        \n",
      "=================================================================\n",
      "Total params: 15\n",
      "Trainable params: 15\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "lr = lr_model()\n",
    "lr.summary()\n",
    "lr.fit(x_train, y_train, batch_size=16, epochs=500, verbose=0)\n",
    "lr.save('iris_lr.h5', save_format='h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate contrastive explanation with pertinent negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explained instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction on instance to be explained: virginica\n",
      "Prediction probabilities for each class on the instance: [[2.2735458e-04 2.4420770e-01 7.5556499e-01]]\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "X = x_test[idx].reshape((1,) + x_test[idx].shape)\n",
    "print(f'Prediction on instance to be explained: {class_names[np.argmax(lr.predict(X))]}')\n",
    "print(f'Prediction probabilities for each class on the instance: {lr.predict(X)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CEM parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'PN'  # 'PN' (pertinent negative) or 'PP' (pertinent positive)\n",
    "shape = (1,) + x_train.shape[1:]  # instance shape\n",
    "kappa = .2  # minimum difference needed between the prediction probability for the perturbed instance on the\n",
    "            # class predicted by the original instance and the max probability on the other classes \n",
    "            # in order for the first loss term to be minimized\n",
    "beta = .1  # weight of the L1 loss term\n",
    "c_init = 10.  # initial weight c of the loss term encouraging to predict a different class (PN) or \n",
    "              # the same class (PP) for the perturbed instance compared to the original instance to be explained\n",
    "c_steps = 10  # nb of updates for c\n",
    "max_iterations = 1000  # nb of iterations per value of c\n",
    "feature_range = (x_train.min(axis=0).reshape(shape)-.1,  # feature range for the perturbed instance\n",
    "                 x_train.max(axis=0).reshape(shape)+.1)  # can be either a float or array of shape (1xfeatures)\n",
    "clip = (-1000.,1000.)  # gradient clipping\n",
    "lr_init = 1e-2  # initial learning rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate pertinent negative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# define model\n",
    "lr = load_model('iris_lr.h5')\n",
    "\n",
    "# initialize CEM explainer and explain instance\n",
    "cem = CEM(lr, mode, shape, kappa=kappa, beta=beta, feature_range=feature_range, \n",
    "          max_iterations=max_iterations, c_init=c_init, c_steps=c_steps, \n",
    "          learning_rate_init=lr_init, clip=clip)\n",
    "cem.fit(x_train, no_info_type='median')  # we need to define what feature values contain the least\n",
    "                                         # info wrt predictions\n",
    "                                         # here we will naively assume that the feature-wise median\n",
    "                                         # contains no info; domain knowledge helps!\n",
    "explanation = cem.explain(X, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original instance: [[ 0.55333328 -1.28296331  0.70592084  0.92230284]]\n",
      "Predicted class: virginica\n"
     ]
    }
   ],
   "source": [
    "print(f'Original instance: {explanation.X}')\n",
    "print(f'Predicted class: {class_names[explanation.X_pred]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pertinent negative: [[ 0.5533333  -1.2829633  -0.5391252   0.92230284]]\n",
      "Predicted class: versicolor\n"
     ]
    }
   ],
   "source": [
    "print(f'Pertinent negative: {explanation.PN}')\n",
    "print(f'Predicted class: {class_names[explanation.PN_pred]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store explanation to plot later on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "expl = {}\n",
    "expl['PN'] = explanation.PN\n",
    "expl['PN_pred'] = explanation.PN_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate pertinent positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'PP'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate pertinent positive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "lr = load_model('iris_lr.h5')\n",
    "\n",
    "# initialize CEM explainer and explain instance\n",
    "cem = CEM(lr, mode, shape, kappa=kappa, beta=beta, feature_range=feature_range, \n",
    "          max_iterations=max_iterations, c_init=c_init, c_steps=c_steps, \n",
    "          learning_rate_init=lr_init, clip=clip)\n",
    "cem.fit(x_train, no_info_type='median')\n",
    "explanation = cem.explain(X, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pertinent positive: [[-7.44469730e-09 -3.47054341e-08  2.68840638e-01  9.17062904e-01]]\n",
      "Predicted class: virginica\n"
     ]
    }
   ],
   "source": [
    "print(f'Pertinent positive: {explanation.PP}')\n",
    "print(f'Predicted class: {class_names[explanation.PP_pred]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "expl['PP'] = explanation.PP\n",
    "expl['PP_pred'] = explanation.PP_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize PN and PP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the generated explanations to check if the perturbed instances make sense.\n",
    "\n",
    "Create dataframe from standardized data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
    "df['species'] = np.array([dataset.target_names[i] for i in dataset.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highlight explained instance and add pertinent negative and positive to the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pn = pd.DataFrame(expl['PN'], columns=dataset.feature_names)\n",
    "pn['species'] = 'PN_' + class_names[expl['PN_pred']]\n",
    "pp = pd.DataFrame(expl['PP'], columns=dataset.feature_names)\n",
    "pp['species'] = 'PP_' + class_names[expl['PP_pred']]\n",
    "orig_inst = pd.DataFrame(explanation.X, columns=dataset.feature_names)\n",
    "orig_inst['species'] = 'orig_' + class_names[explanation.X_pred]\n",
    "df = pd.concat([df, pn, pp, orig_inst], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pair plots between the features show that the pertinent negative is pushed from the original instance (versicolor) into the virginica distribution while the pertinent positive moved away from the virginica distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QkYtZmEOebp63N+DD5HEHJO6/z9q8cKKiY9a+pAQ0DRpg+mZOExp0qWPBswsStrcPtGNfeB8ax0U/VW/vb2c7o0Tp+nVjYYU8CkaCaGlrQUtbS8L2GyM3mu7PGKdkduYMXSGl/KXxjZSyWwhxBQAOhogKQNb1RizMIfd7/KY5QHo4hK6hDgRlGH7hRW2gIe28cNaMoIKXLtfO+JmMAM2/A/5yJ9A2cofjqHnRHKKenaPWG4pvQzPqZ+DyYy9HbVktfJoPnwx8glAkFMvNi39jmS7/gkpAun7dap0hxf7p7mhmIt21xOr+qmsgkN35UWGw8x2DRwgh5Mi8OyGEB0DWPagQ4gEAZwPYLaU8JtvjEVEqq7UalNIV50tS7avC0uOWptR6qPKNQ2vPDiz78/X7z+PUu3B49TSsPG1lyvlV+atYM4IKm5GToarXAqT+7NxVwPPfByonAafcEM0hyqDekJE79Mt//BKXTr8U39vyPdQH6nHt8dfiO1u+k9B+AKClrSUh94hKVKAOuPAh4LHL9sfghQ8B5bXW6gwp4jxdvGZ63akuq1ZeS8xyhsz2r/JXpVwDV39uNYKRIOtwlQDbcoaEED8GcDCi9YYA4EoAO6WUX8/yuP8HQD+ABzMdDHFuMDmkaOey2zaPOsNPCjv7P0bzs4tSHm/d3DVY+Ozi1POYswa14yanfEK3e3C3Msdh3ZnrMLlyssVXoSgVbcwWjXQ5GYD6Z4uejt4RMgZCIzov+wOaRwoTG+LbsZE7ZLSZu5vuxvKXUvf/9Zxfo2OwAweMOwATKybm+o0fY9ZN+ncBT14HHHcJEKgBhrqB1x4BzlqeEn9mOaKx4yTF8mjxmgmr1y6z/deduS7lWrLq9FW4fevtmRybOUMFzs6PTr8JYAmAr4x8/0cAv8r2oFLK/xJCHJLtcYjInG3zqDOcQx40qRsU0iPq85BhaEJLubixZgQVvNFy7VQ/A6K5QhbrDRm5Q8Y+Vf4q07peC55dgM3nb+Yn4KUuHATe2RT9ijfnB9bqDI2hPlYmrF67zPYP6anXkoA3wPyiEmFbLyel1KWUq6WUF4x83SuljNh1/NEIIZYIIbYJIbZ1dOS2AjKRVW6LVyOfIJ6tuQK6Hv1ksGcn0L8L/pF6QsmP59M86vMQ6s9tfB6fyXFYM8JubovZouH1R3N/LvoNsGhT9P9HzQM8/ujdH2PpYoORr2HkcsQx6g3FS27H8W29N9ir3D8iI0WRK8SYHYOkvhoes/j0mcemyhjjdTRWr11m+/u01GvJUHjI2esiuUbWgyEhxJNCiHOEECnvPoQQhwkhbhVCXJ7t44xGSnmflHKWlHJWQ0Nu17knsspt8aqqRWJbroCiTkVtBFh56l2Jj3fqXagvr1Vurw2oX6P6QL2yxgSX27af22K2aATqork/m78NrJ0X/f8pNwDhYeDpG4Av/EJdy8XI0Yv7Wbp6Q4b4tv7AGw/g9tm3J+x/16l34f/t+H9FkSvEmLVIVVMoPKyOz8pJ1uoMjTFeR2P12mW2f32gPmX7lPFTnLsukqtknTMkhJgM4HoA5wPoAtABoBzAoQD+CeAXUsqNWT7GIQCeYs4Q5VlRz2XPejU5M6qciObfQX/vr+g6YQGCmhd+PYzalx+ENvtq6OW1KavJaV7zOz2x1eT0EHwaV5NLUtQxWxTMcobm/RRY/0Vgyixg9rVAZQNQNQUY35i60pzF1bni23q5pxxBPYiQHoJXeOHX/JBC5nPVLMZsvpj01dj0dXvqDDmwmhxg/dpltn8Wq8kxZ6jAZf2OQUr5CYAbANwwMmg5AMAQgB1SysFsj09EuaHKybGFKifCVwHtxZWof3Fl4vYTl0Dz+lA/PnFqQjpezcvFEqhwmeUM+Sqi/27bBjw6P/rva99MfLOpyNFLV28oto9TbZ0Km0lfbVudoTHG66iHtRjPZvubbWdbKX62fnwqpXwfwPt2HlMI8QiAUwHUCyHaAHxPSvlrOx+DiGyiWk1OVaciNIjQ3OXo/NQ8hKUOr9BQ/9Ym+Mzmm8PBO1dE+WRWxyWU9FliunwM7G8fuq5Dhw5d6intJF0bYvsis77atjpDClbjzkpdOcY0Zcr1USGlvERKeYCU0ielnMKBEJFLqeab794ezYlImisemnwcWqd9Fos2fwlnbZiHRZu/hNZpn0WovEZ96JE6SM2bmjHn8Tlo3tSM1u5W6FLP4RMkcoAilwIXPwxUTU3cduFD0bakYLSPW/92K97b+x4WPrMwpZ2ka0NsXwRAHYs1h6lzgwJ16v5ezzxmrMZdWA9jR/cOLHxmIc7acBYWPrMQO7p3IKyHsz42lTbb6gy5CecGk0M4lz2ddPVSkuaWfwwdixR1htbOXYsDxh2Qcmjb6iCVHsZsIUj+hF14gCeWpdZ2OWeFclqS0T5u+PQNyrpB6+etBwDTNpTuZ3loX4zZfFLd7QFStw12mPf3GU6ds9qvfzLwScZ15XJ8zWDOUIFjljER2SNdvZSkueLhvTtN65uo2FYHiciNknMpenaqa7uceafy1432YVY3yGgnY/0ZlRCzPKDkbaPVx8qA1X7dSl05XjPICtsGQ0KI2QC+D+DgkeMKAFJKeZhdj0FE7qCci+31I/yZr6Fz1gKEPB74IhHUb3sQXsXccu9InaHkT+28mld5bL/Hj6YpTTh32rmo8lehN9iLja0bWe+BCpvZp/BGfSFVnoaCUTvFqBuU3K4EBLyaN9aGDqg8ABP8EyBG/gt4A8rfY/sqQZnmAZnluqXJa0uWrl9XXQeMunLJcerTfOgc6sz4msFcIkpm2zQ5IcTbAK4D8AqAWLFVKeUeWx7AAt4OJ4dw+gb2z8Ve9sIytA+0x2ovHDbhULT2/hPXtVwX276iaQWOrDoC3s4dwG8viV44q6citPg5tIa7U/Y9ouoI/Gvvvywcexq8aZbdJsasaxk5dnHtAhc/AnjLgT/eDJx4JfDE1Yk/mzhd+cbUaJO//Mcvcen0S/G9Ld+LtZNbZt+Ch7c/jG+c8A30hfoS2pDxs6XHLcUE/wR8afOXEtrdtJpp+XiTyJjNF7OYVMVdJAzsehN47LL9+174EDDpGMCT2efsRg5Qcr8+rXoa3ut9L/U6UHUYWntaE/a/u+lulHvLsfSPS0fdN92xs4x1TpMrcHYOhv4upTzRloNliZ0eOYQXaZjPxV47d60yD2jd3LWY/KvPp3yCGLryr+iMDCGsh+HVvKgP1KM32Ks89rq5a7FQcez1c9eiXpFjRDGMWbfKpr6QQvxqchEZwScDn6BruAsPvPEAXu98HatOX4Xbt96e0oaMPKN1c9fB6/G64dNyxmy+pMv7TJ4m178LePK6jPPaVMyuJevOXKfMDVo/bz2qy6oT6sr5NT8u2XRJxsdId+wscok4GCpwWU+TE0IcP/LPFiHEjwH8AcCw8XMp5avZPgYRuYfZXOywHjaZzx1Wzi33DffhgOqDMjp2yOTYQZMcIyLXy6a+kEJ8jZSde3diwbMLEn4e8AaUbcjIMwrJECYHWK+rpFnJAwoHLeW1qZj39+rcoGAkmFJXrr2/XX2MNPlFzCWiZHbkDP006ftZcf+WAE6z4TGIyCqLNSDCoSCGerqhRyLQPB4Eqmvg9aXO/zabix2fkxC/3ad5oR99NrqOb0awohb+wS7UvroemmJuuZH7kDonXJ1j5Ne80CNhdA11IKiH4de8qA1EnyfnhJOK1HVEurogg0EIvx+e2loIi7VRbBGfcxF/FyhQG/2+beQuxlHzojlEPTtj7ViXOrqGOqAJD8Q+CRnRoXk9EBV+DIQHY22xpa0l9nBD4SFlG6opr0HTlCb4tMTppsyrKEFefzTeku/2qPKAvH7oJy9D1wkLENS88Oth1L78YLRfz/DaY97f+/CtWTdiXsNpkLqE0AQ2dbygzGEzPUaa/CKz/DhVzANgOygBdk6TO0xK+d5o23KBt8PJIYUzfcPK3G9EB0JdOz/Axrt+iL0duzGhYSLOvf5bqD3o4JQBkdk878OrDse7ve8q8oAOx7963sWyP+/fvvLUFZhWcyS0pLnlZvlIh084DO/2tKYc4/DqI/Buzz8Ttq8+fRWCMmz3nPBCVTgxmwNS1zG8oxVtX70KoY/a4TuwEVN+uQplR07L/YDIaKMtd6TmB527Cnj++0DlJOCUGxLyMvSFm9Aa6cem957BxQ3nYNNPfxRrs/O+/i3c+9FD2PrJ37GiaQVWv7YaLW0taKxsxKozViGsJ7aL+JyhI6qOgH/kTa9ZO8xRG2LM5ouFPCA9HEJrzw4s+/P1cX3yXZhWPQ1aUo6o2bUnFAkp83oOH38Yett2ZnQ9Mr1mVB+Od3vezXq73+NPyUdStANOkytwdg6GXpVSHp+07RUp5UxbHsACdnrkkMK5SFuZ+w2gr2MXfnvLt7C3Y3ds24SGibj4ez/E+IbE/a3O87Y6R9vsE2nVHaCuoQ40J+USmeVGlGhNosKJ2RwId3bi/YsuQuij/bHhO7ARhzz6KLz1eYgNXQf62oE1Z6a21UVPR+8IJf2sc9kraH7hq1jz2Xux6bZbU9rsvO/ejDnPnhPL4wvLMHyaD/WBemhCw66BXSn5RMm1WvJc14sxmy8Wrhud/R+n9L2NlY1YP2cN6n89J6NjfDLwCe7YekfKbIIfzLgp4+sRkOaaYWF7174uZcx/56Tv4Krnr0p8jqntgIOhAmdHztDRAD4FoEoI8R9xP5oAoDzb4xPRGFisAaFHIgkXHgDY27EbeiSSsq/pPG+b5mjH5z4kbPd4UxZLCCpyicxyIzgnnGQwmDAQAoDQR+2QwTzFhqYBUqrbKqD8WVDzoH2gHT7pUbZZn4xe1tsH2iEhcdD4xLy8sB5OySdKrtXCGi0lysJ1Q9X3tg+0IyjVOaKqY4QiIbS0tSRM5wQA/VM3Znw9AtJcMyxsN4v5gDeQso3toPjYcb/7KABnA6gGcE7c1/EArrDh+ERklZGPEC9NDQjN48GEhokJ2yY0TITm8aTsa8zRjhc/Rztlu6bebkcNE/9ILlE8IzfCicejwib8fvgOTIwN34GNEP48xka6tqr4mV+PoLGyESERUbbZkIguKmIW8+naaewxTNo421CRs3DdUPW9jZWN8Atvxscwi0Ur1yO7mMX8UHgoZRvbQfGxc5rcyVLKF205WJZ4O5wc4t7pG8kJq4E6oONtR3KG7JqjbUf+gR4Jo7V7R0rOEDQNbX1tCHgDGAoPYcr4KTh4wsHMGbJJofaxrsoZAqLtdqADCPYDXe8Cf7kT6N8NXLQ+upiC0IDhPuA35wE9H0YXIpm3HAMyjKHgICb0ebHppz9U5gzFt7H4aUE+zYe9wb246k9XJdbsqjkSXi16V4k5QyVK14Gu94Du96KrGoYGgZrDgNrDUq4bqr535akrRnKG3sno2hPWw/hg7wdo72+P9dWN4xoxtXIKund+mHo9mnqILXXlzBZKUMU8c4ZKg52DoZ8junpcvF4A26SUG215kAyx0yOHuPMibbZYQsPRwNAe21eTA8wvJh/s/SB1EDLuIKD7fXT1foBgWSX8wwOorToYmuICO6ann5RLVF1ej3f32l5Ur1C5M2bzyDWryana7UXrgYpa4OkboksWG215/GTokRBaQz1Y1nJtLK5//blfoTLsByI6RNxqcsn5Eao3ebXltdgX2RfLJzIGQrHTy99qcozZfLGy8I6uQ+9tQ5cMIqh54NcjqBV+aFVToj/PYDW5dINuPRLBUHfX/utRTa1tAyGzxwRSV45TbVO0Aw6GCpydg6H7ABwN4Hcjm84H8C8AdQDek1Jea8sDZYCdHjnEnRdpi4slOMU06XruWtQriq46dX55Tv52G3fGLI1edDV+25f/hE6PZ0xxXYDtgTGbL1aLrmZ53clHbDr0mBwMFTg76gwZZgCYLaWMAIAQ4h4AfwXw7wDesPFxiCiexcUSnGKadG1SdNWp82PyNxWE0Yquxm8LBxGEZ0xxzfZAGbNadDXLfj0fscn2QCp2DoZqAIxDdGocAFQCqJVSRoQQwzY+DhHFiy/eaEizWIJVmU6XMSt+59e8yvPTfQF09X+csFR2ct2hsTA9Dya9Uj6YFaA0a7ehwei/jUKsEw4EAPglYvSuwIoAACAASURBVHE9o34GLj/2ctSW7Z8KZzaFzaw9aEJDe387C0mWKlVcmsSk7itHV190pTi/GOmrbbjumBXwdrKv5vWBVOzs/ZYDeE0IsUYIsRbAPwD8WAhRCeBPNj4OEcWraIjO6zZW8DHmeVc0ZH1oY35186ZmzHl8Dpo3NaO1uxW61FP2rfVVYeWpd8VW5DGK8NWW10YL98Wdn978e7Tu243mZxdhzoaz0PzsIrR274AeCWd9zrXltVh52srE8xjJkSDKKSMH41dnAHcfE/3/7u3R7WbttuYw4Kh5wGk3A5u/DdzfBKw9C7W9H2Pl7B+gaUoTrjn+Gix/aTkWPLsAC59ZaNomAXV7WNG0AndsvWPUNk1FyiwuzfrqoQ40b16MORvmoXnzYrT27IBeXpP1dae6rBpLj1uK5S8tx+LNi7H8peVYetxSVJdVO/Cko3h9IBXbcoYAQAhxAIBPj3z7spSyPd3+TuHcYHKIe+eym336nCVL86t726A/8010Hd+MYEUt/INdqH11PbS5PwKe+SZw3CVAoAYY6kbnAceg+U9L1flFSbWExiKPyd9u496YLQWj5VWo2i1gWohVP/eX2F19IBY+f5WlnIf49qAJDXdsvSOhtovLcogYs04zi8vFz0QX78ikr56zBvWVk7O67uQrn82B6wNzhgqcndPkgOidpo6R4x4hhDhCSvlfNj8GESXTNEcWI7A0vzoSgvb2U6h/+6nE7Z+/Pboy1jub9h/32tfM84tsYFZsjyinRsurMGu3JoVYNaFBH+iwnPMQ3x7a+9tTilwyZ6LEmMVlJJR5Xy3DWV938pW/w+sDJbNtMCSEuBPARQDeAmDcb5cAOBgicljy8tKO599o/uini/GfCHp86jnkmjc67Sfu00a/hHquuGZTl+TQnTIiS8aSV2FMFVXlbkgdWmUDHpz7ILqGu/DAGw/g9c7XozlA0KAPdEILDaWNeeZMFLFM+z2zuFT04UaR35R4EV7z604kDPR/Eh1ceXzAuMmA4nqUj5whIhU7l9Z+B8AMKWXeF0vg7XByiCunb5gWv6s5MusBkWlNBt0DbaQQZGyueP1RwO63gMcu27/9woeiNSp2b0/YHl78DHaE9+K6lv3nvKJpBY6smpZ9LQkrtTKKnytjtmREwsCuN1PbxKRjlG8OY/v/ZTlw4pXAE1fHfk+/YC1aPRLLttwUazO3zL4FD29/GJdOvxQPb38YXz36Ukx75iZo/btNYz7PBVUzwZgdC4s1gkxr0yUV69YXbkJrpA/L/nx93PXlLhxefUS0mHZK0dUjoCX192YxH9bD2NG9I/U6EFcAuEBwmlyBs3Mw9AyAL0op+205YBaKvtOjfHHlRbqz/2M0P7sod/k3OqDd36TOgwjUpX4iOLQnZX5652V/QPM/ljszV9wldZdcwpUxWzL6dwFPXpdwVxSvPQKcs0Idi71t+3OFjNXkKhuACQdG6wxtXpzSZladsQo3b7k5dodo/Yyvof7hS9LGvMtz6hizY2G13zO7i6TYruuR6B2guNXkuvZ1qq87c9ag/tdz1PlIRkHWEQVYA8sMB0MFzs6h9yCiq8k9DyB2d0hKuczGxyCiJEE9nNv8m56d5nkQHm/KBU81Pz1YVuncXHGX1F0iQjiYkoMBADjzTvX+kdD+2G3bBjw6P/rvZa8h6FXXGere143XO1+PfR+sGFkVK03MM2eiCFnt98zyfRTbNU1D/fjGhG2m1x1pUlcuEkp5KNb8IbewczD0xMgXEeWQX/Oa1/dxgtU8CMX+/uEB63PFs50Pb1PdJaKMmeZmKHLuNM08787jM8316Q1GS/vNqJ+Bpf97KfTKBnRe+kh0JUfGfOnIcb9net0R6rpy8KROf2bOELmFbffFpZTrADwGYKuUcp3xZdfxiUitNtCAlaeuSKrvswK1gezrDClZrWuk2L+6dpq6voSvSn2MdPVasj0/IqeYxeJwnzqWx01OqfOCCx8Cxk1Grb86pZ2vaFqBja0bMaN+Bq49/lrcvvV2nPnE/0Xz6z9D6xk3QQ/U5emJU87luN+rLa9X15UL1JnGcLJqX5W16wCRQ+zMGToHwE8A+KWUhwohjgNwq5TyCzYcey6AnwHwAPiVlPJH6fYv+rnBlC+uncvu1Gpy5g9ocbW2pP07IdH87MKETxVPm3Iabj3+B9B0DzSvhorxfght5CW3az586XFtzJaM5FgUHsgnrsXgjCuhl0+Etm83Kl6/F+Kcn0Zj2Wwlrv5d0J+6PqGOV/X7W9Hzma8gCInFz6bmExVg7gXAmB27XPZ7/bugb/kFuk5YgKDmhV8Po/blB6HNvlqdO6q4HqXLd62rmIzBviD0sJ56PXAf154YZcbOd0vfR7Tg6p8BQEr5mhDisGwPKoTwAPglgM8BaAPwshDiCSnl9myPTVQsNI/XlsUSMn9Ai/UlkvYP7t2ZcAGcUf+/cfXB/4mNP3kdfXv2YXxdOc76yrGoaxwXvQDaNR+eKNeSYlH2foI9M2/H0+va0bfno2isL7wddREZfUelyrsDgHBQWcer/tNXoN0kn4i5FyUml/1eOAjtxZWof3Fl4vYTl5jHcBKzvCNdCuxp78fT97yhvh4Q2czOjwxCUsrepG2KOSyWfRrAP6WU70kpgwB+C+BcG45LRJnQ9eidmZ6d0f/rOsLhID7pb8fOvTvxSX87wiODEl3q6BzqRHt/OzqHOqFLdRdgzDc3fPWoZXhxzU707dkHAOjbsw9P3/MGBvtG3swZ8+HjFUgekK5LdPQN46PuQXT0DUPX7bkbXwhK+bnH2s3ej6OrxPV8CPS2YTBSOTIQiov1de0Y1MelP16aNmDkE8Vj7aCxK9i4VfTVpiLhaFx2/Sv6/4jFBXds6JOTrwNANG7LwuNiAyFAcT1wSKbXLyo+dg6G3hJCXArAI4SYJoT4OYC/2XDcAwHsjPu+bWRbAiHEEiHENiHEto6ODhselsg5BROvilydcO9O7Oj9JxY+uxhnbTgLC59djB29/0QoPIzW7lY0b2rGnMfnoHlTM1q7W5UXlOQ8p4ayibELn6Fvzz7o4ZE3IQWaB6TrEu/s6sN5q7Zg9p0tOG/VFryzq69w3lzFsRqzxfTcLTPazZPXAZ07ostl330ssOZM6KGIOtZ1T/pjpmkDE3wTsKIpNZ9ogm+CE8+uYIylny3YuLWSV2nUs1pzJrDyuOj/d71pbUBkQ59slu8qdI+yjUTCzg1OjPpbmVy/qPjYmTNUAeAmAJ9HdP7kZgC3SSn3pf3F0Y97AYC5Usovj3x/GYATpZRXm/1OScwNpnwovbnsilydT772KhY+f1XKPO+1c9dgkYW8hfg8p8pITWyKnGF8XTnOv2EmKqvKRn6h8PKAOvqGcd6qLWjrHoptm1ITwIarZqNhfFkuTiFvMeuC554/RruZcwew+dsJ7Wfg4j/i8YfC6WPdjEkb+Lj/Y/zw7z9MWZXrWyd+CwfkcvqsPfLazxZs3FrJq4yvZxW/r6IWUFo29MmqfNe9e/fhCcX14Av/OQPVNaPcQR2jLGsece5egbMtZ0hKOYjoYOgmu4454iMAB8V9P2VkG9ng2HXHZrzvGwvfcPBMyJUUuTohTVPO8w7rEUt5C/F5TlKXOOsrx6bMEa8YHzflogDzgILhSMKbKgBo6x5CMBzJ0xnlTik/91i7CdSktJ+Kv34bZy35HZ6+73/MY92MSRsI62G0tLWgpa0lYfs3TvhGVk+jFBVs3FrJq4yvZxW/r6IWUFo29MmqfNd93n6cvPig2NTp8XXlOHnxQdjn6wfgzGCINY9KW9aDISHEkwBMby/ZsJrcywCmCSEORXQQdDGAS7M8JhElUValV9Su8Om6sr6EV/Oo6054/OpPEIHYNuH1o25yPc6/YSb0sITmFa5dPUjXJfYMBBEMR+D3elBX6Ydmcp5+rwdTagIpnzL7vaNMiXLgXHJttOeuOncArn0+MZl8Gm60m6Hu/e1nyixg9rUQlQ2omzCA879+DPSIgOYBKiYE9se6hU/bjTbrMWl7XqdqjRWxdHHr6pj1+oGj5gHHXRIdhA91A689os7h8fjU+3p8QDgUXQlODwOaN7oSnDe1RpCjNOAXH/wEFy1uRq3vYHSF9uAXH/wE3zrgW+rrlMh+hoBZHS/m3ZUGO3rKn9hwDFNSyrAQ4mpEp915ADwgpXzLycckKjXGfOllLyxD+0B7dO72aSsxbcJh0C58CHjssugbuuqpqJcaVjStwHUt18X2XdG0AvXltVh56l1Y9ufr9x/j1LtQ65sQnbv+20tix8DFjwDecuA358W2iYsfQeXE6a6e+mbkE1zx4Da0dQ9hSk0A9y+YhaMmjVe+Aaqr9OP+BbNS9jfeROXyXHIt3XNXnfuDl38aw2Hdtc8HwP68jORYTo5bI5+i5Q7gC78A/n4vcOKVwBNXAz0fQlRPReW5q4Dnvw/0747WYZl0DCC0zI6PxDZ74uQTcdepd+H6uLa3omlFIS6rnXdmcVsT8CnbW5lXw4IHXsp/zAbqgFNuSOirceFD0e3JKiep962sB3a/lbp94qdyOiCqD9TjyuOuTLnG1JXXqa9TNdOyHhDVltdi5WkrU45dW15r07MiN7MtZ8hNXJ2D4TKcJmdJ0eYMmc6XnrsW9U9+PeUTxPD/XYXOyCBCegQ+zYP68np4B3ZDf+abCXVQal9dD23uj4C1Z6XOT5/3U2D9FxO3mdUNcomx5BM4dfcmw3PJa8yaPXfVua9ZdAK+u/FNd+dqWMnLMO7w6Hr0U3ZVG5hzB/Do/P35Gh5fxsdPbrPnHX4eFh2zCD7NB6/mRX2gHj5Pjj/Rt0fe+1lV3O4ZCCrb223nHoPFa19O2JaXmLUSm2b7LnpaHaeLngaqD0IuhfUwOoc6EdJD8Gk+1Afq0TPck01ez6iyuOvkkk9raKx4D52IzOdL62HgnU3Rrzje4T5MTr44RkLKOij4/O3q+em+itRtZnWDXGIs+QSaJhx5Y1QIuQ1mz1117hV+j+ufj6W8jPh8ip6d6t8L1Oz/dyQESJnx8ZPb7IZ3N2DDuxuw+fzNhbhogquo4tasvVX4PSnb8hKzVmLTbF89bL49x7yaF5MrJydsczqvRxMa76aWKA6GbHLIjZuU29//0bwcnwmRdabzpTVvSs4QqqcCvkD008X4vAaPD/rRZ6feGTI7Rmgw8SQKoG6Q0zlAhXouVqnOfTAYcf/zUeTQxZYW7t+VmN8Tn/sjhPr3hrr3/9vjM8/lSGoXutShCQ0Pzn0QXcNdeOCNB/B65+vMcXCQWXsbDCYOfPIWs2ax6fGn9tVm+5r11Wa5Zzle4dPv8aNpSlPKqomMecqWeyfnE1HO1PqrlfUeasvrU2tJzN8A9H2SUs9Cr5yI1jO+jebXf4Y5z1+B5td/htYzvg193MTovPP4Y1y0HqiamrjtwocAl8/PNvIJptQEAMDWHKBCPherVOd+cF2F+5+PqrbKuauA3y9KrOuSXPNl672pbeDcVcCWu+PyNSbtz/vY/G1g7bzo/0+5ISHvw8gVWvjMQix4dgGWv7Qc1xx/DZqmNDHHwUFm7e3gugp3xKxZ3Z/hvtTaQ4E69b7jGlLj9MKHotuTWalrZJPqsmosPW4plr+0HIs3L8byl5Zj6XFLUV1W7dhjUmnIOmcoB6vJWZaPHIxCvTPEnCFL8j6X3TH9u6A/dX3qXZ2z74peZOM//ZMAfp0637zzy8+h+dlFmeUdTTo2+kYv+RPws5Zbq3ORB25awS2Dc3FtzLp6Za50jE/DQ0PRgqp/uRNoG3ktjBwNIDEn46LfROP7uEuA+iOjb1Ar6qLTj/QI8I/1wOyrU38v/pgjU+7M8vvWnbkOEysm2rKyVp4xZsd+gol9tfAA9zep4ym5X69oAPZ+BPz9PuDfmgHNsz82T1ySmjNkJUfJJlnWAnKSyzopssr1q8kRUQ6Eg+p8n7k/Sq0lYZL/ENTDmecdXb1NmYuEOT+w4ck4y6kcoLFw07lYZXburn8+Rnvo2Zm4AAiQmKMR30YCNfvjfdGm6F2fZCcuSf295GPCPG/CmDpHznF9zGbYVyMcVNcI0sPAiyujX/FOuDz1sazkKNmEtYDIKVkPhqSUf7HjRIjIZmbzuSPhaB2JSCiaozBucvoaFcnH8ajnm/s1b+Z5R1KazG93dvUrN93VUXH7+Y1V/PPyeTV4NYGhYIE/x/i8i5EaQphwYPRnQgOafxe9awQAFfXA5ZuBgY5ou1PFvhDRN6PG78XfbYrLGWI9FOeFwzp29w8jFNHh82iYOK4MXm8BDDST+2pfIPN+vaLBWs6QWd6Rg3mfjH1yim0LKAghpgH4IYDpAMqN7VLKw+x6DCLKkFk9lPqjTOpITFfXnSivTT3O/A3RYyUduzbQoK7TEGhI3d9bHj1+8uONmzz6cxvzS+LuujxuP7+xUj2vH18wA8uffQcd/cOF+xzjawnF1RBC9dRobaFta4EzfxydTvfwF/f/7IK1wHn3ARuWJMb+0zdE7xwZ+URGDaKLH9lfpBish+K0cFjH27v6sPQ3r8TidfX8mTh60nh3D4iUff7DwOk3J8afWb9+8SNA/REm/fLE1MerUPTrSbFqN8Y+OcW2OkNCiP8G8D0AKwCcA2AxAE1KebMtD2ABc4Yyx5whS1w7lz3FWOpIqLYvfgZYc2bq9itaABlJuetkWqdB9Smk1FPvUHmcW+ByLDWCcsmh88t7zJo9r++ePR1XPvSKq/4Gluk60NeubiNz7gC8ZcCmr6f+7PLnonePIiMrzRkDofh9Fj1tukJXFvVQCkFeY7a9ZwgX3vtiSrw+duXJaKwOOHFq9jDr81X13Mz69UVPA898M/NczhyvJge4NvYL7JMcSmbnO4+AlPJ5IYSQUn4A4PtCiFcA5HwwRFTyxlJHQrU9ElJvDw0pi/CZ1mlQzU+HltPFEtxel8ft5zdWZs+rOuCL/btgn6OmmdcGiq8hlPyz8D6g9tCR73em5s4Zv2OSiM56KM4JRXRlvIYjzq2SZguzPl9Vz82sXzepK2eay6ns153F2Ccn2DmcHhZCaABahRBXCyHOAzDOxuMTUaaM+dzx4utIZLrd41Nvd3k9IBWjTkg8N9Wxcfv5jZXZ8+oZCsX+XdDP0aytDXVHa2mZtavRfr8A21gx8Hk0Zbx6PXm/+5CeWRyp6rmZ9evprgNERczO1v01ABUAlgGYCeAyAAttPD4RZcqs5sS4ySZ1JCab76/abse8cF2PTu3o2Rn9v431KcJhHe09Q/hgzwDae4YQDutjqsuj6xIdfcP4qHsQHX3D0HXzacWqx7SikOsGpaN6Xj++YAZW//ndWD6GlBLhsJ71a5gXqrb2hV9EpxfVHBZdVlvV3tL9/kXr97cPB+u2UKqJ48qwev7MhHhdPX8mJo5TT+MMhSL4qHsQH+wZwEfdgwiF8nSX06zPrzks83493fVBxcE+nCiXbMsZih1QiAkApJSyz9YDW8CcocwxZ8iSvOdfWGJlNTmP13x/J+aFmy3wMHF61sdOlwCtaSLj1dqsLGhgV9K1A6vJ5T1mdV3i/T0D+GDPICr8HkgAB9dVYF9Ix/udA1j5fCs6+oexev5MlPs0LFrzcmElrgOJbUSIaH0XTcs8Ny7+9/UwsPmm/Ysp2NQuCkheYzYc1tG+dwjBsIQmAF0Cfq9A44RAShyGQhG8vbsfX4lr9/fMn4mjJ46Dz5eHu52qvhqw1q+bXR9Uj+VQH16AmDNU4OxcQGEWgDUAxo9s6gVwuZTyFVsewIKiHAx9v2qUn/eO6bBODoaKcKCV9zeWRcPBgn12JUBbWdDAxUnXeY9Z1eu4ZtEJ+O7GN1Ner9vOPQaL176csM0Fr2Hu5KGQpQsVzAIKH3UP4qL7tqbs++iSk3BgTVKuTrFhrMbjYKjA2bmAwgMArpJS/hUAhBD/jujgaIaNj0FExcDBgn12JUBbWdCgYJOuc0D1Olb4PcrXq8LvSdlWUq9hHgpZUiIrbTmsS/W+aabTFg3GKhUROwdDEWMgBABSyv8WQoRtPD4RFQsHC/YZCdDJn9ZaTYD2ez34/PSJOH/mQagO+NAzFMLjr+xUJvvb9ZjFyFhAIf61GQxGlK/tYDBxoFlyr2EeCllSIp9HU8amKg69mlC3+0KrmTUWjFUqInZeZf4ihLhXCHGqEOIUIcQqAH8WQhwvhDjexschokJnluxrw8IMVhOgzdQEfFh2+pG47antuOi+rbjtqe1YdvqRqAmkrqxk12MWI9UCCkdMrMQ1Sa/tNacficMnVpb2a+hgu6DMNFT6lbHZoFjIZOK4MtyT1O7vKZWYZaxSEbEzZ6glzY+llPI0Wx4oA8wZyhxzhizJe/5FUXGwYF84rGN3/zDCER1ej4aJ48osJ+FbLYJqx2M6wBUxm7wwhITEf6z6W8pr+4evfAZhXbrtNcytPBSydJm8xqzVdh8KRaLtXpfwagITx5XlZ/GEfGCsGkrgVmBxs22anJSyya5jEVEJcLBgn9erZZ10b7UIqh2PWaw0TSS8kfyoe1D52oYievEnno8mD4UsaT+r7d7n85RuzDJWqUjYNoQXQkwSQvxaCPHMyPfThRBfsuv4ROQiLq8vYVYfyErdICeLoFo5j2Kj6xJCiKIsMJvA5W2E1O2wWIsfZ4WxTEXOzvuZawFsBtA48v0OANfaeHwicgOjvsSvzgDuPib6/93bXXOBNOoDnbdqC2bf2YLzVm3BO7v6EA7ryu1mAxGniqCanV8pDIiM575uy3tY1Xx8Sq5Fdbmda/rkkcvbCJm3w6oyjzIPqGhi0yrGMpUAOwdD9VLKxwDoACClDAPIUylmInLMYMf+QntA9P+/vSS63QX2DARjhVKB6BSXKx7cht39w8rtewbUS8FqmsBRk8Zjw1WzseWbTdhw1WxlwVW7zs/sPIqJ8dyPP6QOv3ihFd89ezoeXXISvnv2dPz8+R3oKJbXwOVthMzbYcdAED9/fkfxxqZVjGUqAXZ+1DEghKgDIAFACHESooVXiaiYuLy+hNmc/7BJ/RCzXAAgNdfFyfNLdx7Fwnju1QEfntu+G89t353w8+/MK5JPm13eRihNP6HL4o5NqxjLVALsHAxdD+AJAIcLIbYAaABwgY3HJyInZboykMvqSySvVObzmtcJUdUESZcLkHzsuko/IhE9q9WjVHV3SiUnwXjuPUOh2N+osaoc5T4PBoMReDSBroFhDAWjr3dNwIfuoVDC65/tnTklu1fFclkboVRm7dCrCVz52UNwwayp8GgCEV3i99s+hNejxVaMDEV0+EZWOwSQss3r1ZR9hyOxO5psY5uxTCXAztXkXhVCnALgKESXGXxHShmy6/iFyvEltzNltjT3oVNzex7kTsa8cGM6hFEzYuL01AunUV8ied881Jcw5v0b012m1ATwyBUnYtnpR2Lpb16JbVs9fybqAj7cM38mvhK3PV0ugNmxe4bCKcc4euK4jAdERi5S/HHtyEUqBNXlXtwzfyaeeq0NV582Db94oRULP3MovrL+1dhr8eMLZmD5s++gYbw/5e94/4JZtkxVTGAl9jPlojZCaqbtMODH2cdNweK1Lye08dpyH97e1ZcQjw9fcSL2DoVT+pqjJo7DPzsHUo5te+yOxo7YZixTCbCzztAXATwrpewTQnwHwPEAbpdSvmrLA1jgpjpDZiwPhrKtM2Ty+8daGAyxzpA7arY4on9XNDE2+dO/L/9JvXSqS+pLqGqCrFl0Ar678c2UT3wfXXISbnnyrZQ7Rt//wjHKJbFVx/7T9adg0ZqXlMe2srxuDj81dlXMtvcM4ftPvImbz/kULr5vK7579nTc9tT2lNfzu2dPBwDlz8zqvYyZ1djPlEvaSAHKWcyq2uHHvUO46L6tyjaevD1df6A6hu2xOxq7YpuxPBrWGSpwdk6T+66U8ndCiH8HcDqAnwC4B8CJYz3gyADr+wD+F4BPSylLsDIlUQ5YnRfukvoSqnn/FX6PLbkAqmNrAqbHtsKJXKRCEIroeG77bnx73vRY7pDq9awO+GL/Tv6Z7blVTuVEuKSNkDlVOwzr0rSNW+kPXJEXaFdsM5apyNk5GDJa+TwA90spNwkhbs/ymG8C+A8A92Z5HKLCYscncVaOkYd54Vbvjqj293s9KflB2kgNGyu5AGbHTj6OLmF6bEplvK66riMiox+f/u7Kk+HTBH6/9GRUBXz4/PSJCQNUI6fI+LfjuVWjxX5yOwrUAUN7+Cl5kfJqQp1zqNguhHl/YBa7jt4VjoSB/k+ASAjw+KJfzPchGpWdg6GPhBD3AvgcgDuFEGXIculuKeX/AIAQfKNBJcSOed5Wj5HjeeGqfJx0c+rN9j+ivjIlr+Te+TOxZvEJWLwmcc6/WS5AXcCnPPa0hnEpOQVlXqHMOzISqWk/42+24o/vYOFnDsU3H38dDePKcMPco2JTiIzXDwCe27479v3/fNSDP729G6vnz0zJGbI9typd7Kva0YUPAX9ZDryzyZ78InKVuoAf15x+ZEobV21fu/iElBi9Z/5MNJjkI9WY9DW25BJFwsCuN4HHLtsfq5f+Drj4YeC3lzLfhygNO3OGKgDMBfCGlLJVCHEAgGOllM/ZcOw/A/jPdNPkhBBLACwBgKlTp8784IMPsn1YS5gzpDg2c4bMD5QuXu2Y5z2WY+RwXrgqHyfdnHqz/R+78mRceO+LKdt/8sX/jd6hUMInu98751MZ5wIY51JX6bd9Nbkcy03MKhh/s/i8oHsvm6nMA1qz6AR0DQQT/lauWE3OrB3NuQN4dP7+77PNL6J4eYtZAPioe9BSP7HiwuPQNRhM6Gt+cN4MZd+xZyBoqd+zpLcNWHNmaqx+6Y+AELyT6Sx+Yl/g7FxNbhDAH+K+/xjAx6P9nhDiTwAmK350k5Ryo4XHvw/AfUA0UTLT3yPKh7Txasc877EcI4fzqw234gAAIABJREFUwq3W2jHbP2RSO0gAuPKhVxK23zSSp5K8b7r5/aqcAk3zWFosoVhY7WPjawoZr69ZjlDXQBAX3bc1tu2medNx4MjrnpPcKrPYN2tHgZrE71lzxZXG8r7ASs5QW/cQdClT+prvnaPuOxytMRYJqWM1NATUHpr98YmKmJ3T5MZESnlGvs+ByFXsyN9JdwyTT8F1qaNrXxeCkSD8Hj9qy2uhCWc+QUxXa0dVy8Nsf59J7aDBYOKbi3Tz+M1yBEqh7o+T4msKGa97/L8N8TlCxveuycEya0dD3Ynf25iDkct2WOpUfU26fiLTvsas73C0xphZfpDHl/2xbcLYJrdiFBK5jZHDUD0yhXEs87zNjhGoi+ZA/OoM4O5jov/fvR16JIzW7lY0b2rGnMfnoHlTM1q7W6FLZ6qu1wR8WD1/JqbURJe0NupzVJV58PauPlx474s45cd/xoX3voi3d/WhutyL+xfMStj//gWzMHFcWer2y2bhoNpAyrEbKv3Kx2yojNazue2p7bjovq247antWHb6kagJuOdNRCEy6rg8/spO3Hn+jOjr/ed38eMLZiT8De6ZPxOPv7Iz4XvX5GAF6qI5QvHt6MKHgNce2f+9jTkYutRz2g5LWTisK/uauoAf9yT1E0bOkKoPOriuImWbWV6b0SYy3d+SyknqWK10x/RNxja5mW05Q04QQpwH4OcAGgD0AHhNSjlntN9jnaHMf585Q5bkrmaLU6vJDXYocyA6v/wcmp9dhPaB9tjmxspGrJ+3HvWB+iyenVpH3zBu2vB6yt0Ys7yex648GZMnlCtXYVKtzqTrMprXE9HhjasKb3wSHL+9eyjk3Dz+/MtrnaHk1eTCER279g5jOByBz6OhZyiEV9/fgwtmTUXvUAgHVJW7Kwerfxfw5HXAcZdEp8YNdQMfvgScdCUgpe05GJ1DnWje1JyzduhSOYnZ9p4hZb7ho0tOwoN/+1fKqpMLZx+m7IMAZL0qpi15cP27gC2/AP6tGdA8gB4B/rEemH21K/LZijy2XXIrm8Yq79Pk0pFSbgCwId/nkROjDXaotNiRv6M6hkkORFAPJ1ykAKB9oB3BiDO5EMFwRFnzxzSvJ6Kb1uZR5/UIZSFVr1dL2e7oPP4Sl/y3+WDPAL5474sp+532vybjovu24r++cap7BkJAtL28syn6Fe/EJUD1QbY/XDASzGk7LGVm+YZhXeLev76Pe//6fsLPmk86xLQPsvKhiWM1xsJB4MWV0a94Jy6x/7HGgLFNbubqwRAR2cwkB8KvedFY2ZjyqZ3f40w9Cr/Xo675YzZf32Ptk3dlLoBXfQxH5/FTAp9HU+ZnGblEHk0gHNZjfytHa7JkIsf1t/wef07bYSkzyzdM1wflPR7TyUOtOCsY2+RmzBkiKiUmuUS1gQasPG0lGisbAUQvUitPW4na8lpHTqO63Bur+XPaT/+CxWtfxtnHTTGdr99gYU69WS5AOKyem+7oPH5KUF/hwzVJ+VlXnzYNr76/B3eePwO3PPlW7G9l1Ck6b9UWzL6zBeet2oJ3dvVB13M4tduO/D0Lastrc9oOS1lDpXlukGp7bbkv//GYTo5j1SrGNrmZq3OGxqogc4aynSbHnKFcyGv+hW1csJpcuvn6tzz5Vsqdgx+cNyPjqSVmx37sypOVU+cAF9yBcI6rYtbsb7Nm0Qm44fev4x87e2J/K59Hc0cuVw7rbwFccQs5itl0eYuqPsgsn9FVuYU5jlWriji2i+JiUco4TY6o1JjkI2lCy1kia7r5+qpcou+dk3n+jumxI+arFjk2j58SmP1tugaC+MfOntj34YgOKc3rP+VUDutvAblth6UsXd6ilXxGV+UW5jhWrWJsk1sVxZCciAqLMV8/Xvx8/eTtVvJ3TI9tMe+I7Gf2t0mpM+TRYrlcyfsyl4vsYBZfZn2QHX0TEbkT7wzBfIqb2fLXVqfEZSujqWyjTUmzMB1uTFTT8Cw8ppUpdYC1aXVOHpvGZuK4MqyePxNLf/MK2rqHYjV/jLpBVzy4LbbdyN/JdFGEdMem/DCmIfo9AvfMn4mvxP1t7pk/Ez9/fgcAJPytNE2YxgJRtoxcweT4MuqRJfcfDSb7Mx6JCh8HQ0SUc16vhqMnjcdjV56cUgvoqEnjseGq2Sl1g97e1ZfyBuXoSeNTBkTpjk25ZyyEYLyJvPKzh+C3S06Crkv4PBrCuo5LPn0wvvTvh2EwGEGF3wNNE9A0oYyFIsnlojwziy9dlyj3abjt3GNQ4fdgMBhBuU+Dpqn7JsYjUeHjYIiI8kJV8wdQ5+98sndfbCAEROfqL/3NK6aLIpgdm3Jvz0AwNhACgHv/+j42vbkLG66aDQD4omJBBSMpnblc5CSzvmbRmpdNF2BhPBIVHw6GiMj1xrIoArnDaEVtXZ+UTiWFfQ1R6eFgiIhcRbXMtVmxTrNFEawulV3ES2vnjNlrmFzU9sKZU7DklMMR1iW8msDnp09MWLmLSemUT+n6GvYTRMWppAZDTi588H75pekfe9/Dac/l/XLbT8l2aRcicHqBBioJyfklRpLy4XUVuOb0I1MS71XFWM2OcdSk8co3Llb3p1TpXsP4RPXPHFaH+ScfjEVrXkr4OwLAc9t3Mymd8q4u4FP2NUbRVfYTRMWHGcVE5BrJ+SVt3UO44sFt6BwMxd6cGNu/8ptX0LMvnPEx9gwELT2m2f6UKt1rGJ+o/rUzpuGq9a+m/B2/c/an8PulJ+OxK0/mm0vKq87BoLKv2TPEfoKoWHEwRESuYZZfYjaPX5VbMlqOSrb7U6rRXkMjUT2sqwuphiM6Llj9IqSUHAhRXpnGqMl29hNEha+kpskRlZJCnN+enF8CRHNIjGKdydtVuSVmxzDLQ7G6P6Ua7TU0YtEzUrgyeb+ILvmakyt4TWLUyG9LziVizBIVPt4ZIipCRg7Heau2YPadLThv1Ra8s6sPui7zfWppGfklRqV3Y16+UYw1ebsqt8TsGGZ5KFb3p1TpXsP4WLzm4X/gxxfMSNhvVfPx+P22D/makytMHFeGe+bPTIjRe+bPRH2FH8tOPxK3PbUdF923Fbc9tR3LTj8SNQFfns+YiLLFO0NERcgsh8Oo3+JW6QptZlrw0GqxThb3zF6617CjbzgWi23dQ1j+7Du47dxjcHhDJTyaQJlXw5f/zxF8zckVfD4Pjp44Do8uOSm24uHEcWXo2RdW1jpze59KRKPjYIioCBVyHoxZoU0rBTitFutkcc/smb2GybH4j509WLz2ZWz5ZhMOrKnI5SkSZcTn86TEZrB/uGD7VCJKj4MhoiLktjwYVf4SANYCKgF+r0eZawEAHX3D/DuS64RCEezuH064M+S2PpWI7MPBEFERiq/tEl8TIx85GaoaNA9e/mkMh3XWAioB1eVeZd2WP7zShkdfaePfkVwlFIrg7d39KfF6VEOla/pUIrIXF1AgKkLxORxbvtmEDVfNztsbTlX+0gd7BlkLqER0DKjrtsw99gD+Hcl1dvcPK+O1YyDomj6ViOzFO0NERcoteTCq/KUKv4e1gEqEWY0oz8ibSP4dyU3S1RlyS59KRPbiYCiNQ27clO9TIBPHrjs236dAGVLNtR8MRlgLqIjF53eZ1W2JjCzzzr8j5YsqDzFdnSEiKk6cJkdEjlLVoDliYiVWJ9XyWD1/pmnNDtYCKhzJNa4e/Nu/Uuq2rGo+Hvf/13v8O1LemNVia6j0K+sMTRzHO0JExYp3hojIUaoaNBISK5/aju+ePT22wtjK53fgB+fNMF1Wm7WACkNyfte9f30fAPDokpMQ0SW8Hg0Vfg1fO2MavuE9mn9Hyot0tdhUdYZ8Pt69JCpWHAwRkeOS59p/1D2I57bvxnPbdyfs971zzHNHOF+/MKjyu+796/tY8JlDMbWuMratmiWGKI/S5SH6fGWsgUVUQlw9TU4I8WMhxNtCiNeFEBuEENX5Piciyp6RAxSPuSPFgX9bKgSMUyIyuP3O0B8BfEtKGRZC3AngWwC+medzUjr20Klpfz4eNyZ8/8a/PnTydLD0xZ/F/r365K+N+TiLnj449u+1Z32Q1TkRGdxUB4nsxb8tFQLGKREZXD0YklI+F/ftVgAX5OtciMg+zAEqXvzbUiFgnBKRwdWDoSSXA3g03ydBRPZgDlDx4t+WCgHjlIgAFwyGhBB/AjBZ8aObpJQbR/a5CUAYwPo0x1kCYAkATJ2afsoaUb4xXqnQMGap0DBmiSgTeV9AQUp5hpTyGMWXMRBaBOBsAM1SSpnmOPdJKWdJKWc1NDTk6OyJxobxSoWGMUuFhjFLRJnI+52hdIQQcwHcAOAUKeVgvs+HiIiIiIiKh0hzsyXvhBD/BFAGYM/Ipq1SyqUZ/F4HgA8A1APodO4MR5XPx+dzt1+nlHKu3QeNi9ds5ftvbpdieB5ueQ5uj1mr3PK6psNzzE4xxKybX99kPNfsORKzlDuuHgxlSwixTUo5qxQfn889f889X4rleRfD8yiG5+BGhfC68hypkF5fniuRC3KGiIiIiIiI8oGDISIiIiIiKknFPhi6r4Qfn8+99BTL8y6G51EMz8GNCuF15TlSIb2+PFcqeUWdM0RERERERGSm2O8MERERERERKXEwREREREREJYmDISIiIiIiKkkcDBERERERUUkqysHQ3LlzJQB+8cvuL0cwXvnl4JcjGLP8cvDLEYxZfjn4RQWuKAdDnZ2d+T4FoowxXqnQMGap0DBmichMUQ6GiIiIiIiIRsPBEBERERERlSRvvk+Axk7XJfYMBBEMR+D3elBX6YemiXyfFhHRqNh/UTFgHBMVPg6GCpSuS7yzqw9XPLgNbd1DmFITwP0LZuGoSePZERORq7H/omLAOCYqDq6fJieEKBdCvCSE+P+EEG8JIW7J9zm5wZ6BYKwDBoC27iFc8eA27BkI5vnMiIjSY/9FxYBxTFQcCuHO0DCA06SU/UIIH4D/FkI8I6Xcmu8Ty6dgOBLrgA1t3UMIhiN5OiMiosyw/6JiwDgmKg6uvzMko/pHvvWNfJX8uu5+rwdTagIJ26bUBOD3evJ0RkREmWH/RcWAcUxUHFw/GAIAIYRHCPEagN0A/iil/LtinyVCiG1CiG0dHR25P8kcq6v04/4Fs2IdsTFXua7Sn+czo0yUWrxS4bMzZtl/US443c8yjomKg5CycG6yCCGqAWwAcI2U8k2z/WbNmiW3bduWuxPLE65ik3OOvLilEq+UF66NWfZfZMK1MavCOCY4FLOUO4WQMxQjpewRQrQAmAvAdDBUKjRNoGF8Wb5Pg4jIMvZfVAwYx0SFz/XT5IQQDSN3hCCECAD4HIC383tWRERERERU6ArhztABANYJITyIDt4ek1I+ledzIiIiIiKiAuf6wZCU8nUA/5bv8yAiIiIiouLi+mlyRERERERETnD9naFSxlVqiKjYsF8jt2OMEpUWDoZcStcl3tnVhyse3Ia27qFY/YKjJo1np0xEBYn9GrkdY5So9HCanEvtGQjGOmMAaOsewhUPbsOegWCez4yIaGzYr5HbMUaJSg8HQy4VDEdinbGhrXsIwXAkT2dERJQd9mvkdoxRotLDwZBL+b0eTKkJJGybUhOA3+vJ0xkREWWH/Rq5HWOUqPRwMORSdZV+3L9gVqxTNuYt11X683xmRERjw36N3I4xSlR6uICCS2mawFGTxmPDVbO5og0RFQX2a+R2jFGi0sPBUI5ZWbJT0wQaxpfl+AyJiJxj9GtGX/hx7xDfcFJemF2Pee0lKi0cDOUQl+wkImJfSPnHGCQiA3OGcohLdhIRsS+k/GMMEpGBg6Ec4pKdRETsCyn/GINEZOBgKIe4ZCcREftCyj/GIBEZOBjKIS7ZSUTEvpDyjzFIRAYuoJBDTi/ZaWWlOiIip6VbrYvLF1M+pYtBXkuJSgsHQznm1JKdXBmHiNxktD6JyxdTvqlikNdSotLDaXJFgivjEJGbsE+iQsS4JSo9HAwVCa6MQ0Ruwj6JChHjlqj0uH4wJIQ4SAjRIoTYLoR4SwjxtXyfkxtxZRwichP2SVSIGLdEpcf1gyEAYQBfl1JOB/D/s/fmcXLUdf7/8119ZDozIZmZngBDQK4ERC5J3DXiagDd5YesWQ2CLkHQXTGAGwl4rKtfgf2y68FCMCpE/eoiBPEgukHxQAUUAV0IIpfACAiEiWQmk4RkMjN91Of3Rx/TR1V3V3f19PV+PtKPTNfxqeqZd7/q8+mqevbrgYtE5KgG71PToWYcRVGaCc0kpRXRulWUzqPpBQrGmK3A1vTPu0Xkj8ABwBMN3bE6kEjYbNszRTxpEwpYzO+ZRTBY2XhV7UyKojQTuZlk2zZJA8akLF1u2aQWL6XReD2W1nLcVhSlOWj6wVAuInIw8Frgdw7zzgfOBzjooINmdL/8IJGwefLl3azasDlrsFm/cjFH7jvH04BI7UytQavXq9J5VFOzliX0d4crsnOpxUvxm2pzttJjqR/HbUVRGk/LvFtFpAfYCFxsjHmlcL4x5qvGmCXGmCUDAwMzv4M1sm3PVDZQIXXD5qoNm9m2Z6rBe6bUg1avV6XzqLZmK7VzqcVL8Zt656wetxWlPWiJwZCIhEgNhG42xny/0ftTD+JJ29Fgk0jaDdojRVGU2qnUzqUWL6XV0OO2orQHTT8YEhEBvg780RhzTaP3p16EApajwSYYaPo/kaIoiiuV2rnU4qW0GnrcVpT2oBXesScC5wAni8jD6cdpjd4pv5nfM4v1KxfnGWzWr1zM/B69B0hRlNalUjuXWryUVkOP24rSHjS9QMEY8xugZe+ejceTbNszRcI2BC1hfs8sQqHiTzqDQYsj953Ddz+4lETSJliBlUbNS96wjc3Y5BixZIxwIExfVx+WWBXPVxTFO252LoCR3VN5prn+7jDfv+ANTMSThAIWA91hzbgmp1Rutmqmuh1bncxxR8zv4Tvnvz7vGK/yhPrhpaZatf6Umce3wZCILAH+BhgEJoDHgJ8bY3b4tY1WIx5P8uS2PVyQY5q5fuVijpzf4zogGpwXcWipGDUvecM2NkM7hlh952qGx4cZ7B5k3cnrWNi7EEussvMVRameQjtXJr/W/vwpzn3DIXx84yPZHLvqjGP5/E+fYmBOmNWnLMozdWnGNRelchNoyUx1O7YeHu3mqW178urxhve9jnjC8IGb9Dg8E3g5TusxXfFCzRUhIu8TkYeATwAR4ClgG/BG4Bci8k0R6Uh38LY9U9mBEKRurLzAJ9OMmpe8MTY5lg1FgOHxYVbfuZqxybGK5iuK4h+Z/Fqx+MDsQAhSOfbRWx9h1bLDWLH4wCJTl2Zcc1EqN1s1U92OrU7muBfHJrIDodxltUbrg5eaatX6UxqDH2eGZgMnGmMmnGaKyPHAQuAFH7bVUiRs42yasU3Nbat5yRuxZCwbihmGx4eJJWMVzVcUxT8y+TUvEnLMsXmRUPbnwnmacc1DudxsxUx1O7Y6Hc9nhwNaozOIl+O0HtMVL9R8ZsgY82W3gVB6/sPGmF/Wup1WJGiJs2nGh9Pnal7yRjgQZrB7MG/aYPcg4UC4ovmKovhHJr92TsQdc2znRNx1nmZc81AqN1s1U92OrU7H872xpNboDOKlplq1/pTG4NuFkyJyiIhcIyLfF5HbMg+/2m9F5vfM4voC08z1Pplm1Lzkjb6uPtadvC4bjpnrh/u6+iqaryiKf2Tya+PmF/ncimPzcuyqM45l/d3PsHHzi0WmLs245qJUbrZqprodW53McQf2RfjaOXocnim81FSr1p/SGMSY2i/ZAhCRP5D6PqBHgew3jhljfuXLBjywZMkS8+CDD870Zh2JxRKMjMeyppmB7jDh8PTVibnWmlDQImgJE7HKzElqk/OGDza5uvxym6lelbajaWu2MBsDlmBZkpeBvZEQOybimnFNTB1scg2vWTcLbMYml2t8BYqmqU2ufjSpTU5DqcXxU609aYxZ52N7LY9tG57ZvtfV+OZkrcmYlEb2TJW10hQampTSWGIRjUSrnq8oij/E40meGhl3N212Ty+rGdfclMrNVszURMIussatX7mYI/edU2R8VavrzOOlplqx/pTG4OcQ+QsicpmILBWREzIPH9tvOcoZ35zmZ0xKaqVRFKVdqadpU1Fqwckat8qlNtXqqijtgZ9nho4BzgFOZvoyOZN+3pGUM765zc81KamVRlGUdqOepk1FqYV40nauzaRdtKxaXRWlPfDzzNC7gEONMW82xpyUfnTsQAjKG9/c5u+ciBctqyiK0i7U07SpKLUQCljOtRko7i6p1VVR2gM/B0OPAfN8bK/lKWd8c5qfMSmplUZRlHalnqZNRakFJ2vcepfaVKurorQHftrk7gaOBR4AshfXGmPe7ssGPNBMdq5yxrdabHLKjNNwy5GieKRpa9bN2KV0PA2vWSdrnJshTq2uCmqTa3n8vGfoMh/baihewq1wWScVbCkbkqMRrtt52U5kBtWYiqLUQKW5meloxpM2oYDFAXO7VEXc5LRrDper2Uo+Klara3vTrrWv5OPnYOgFYKsxZhJARCLAvj62PyN4UWU6Lbt+5WLW/fJp7nhim2o2a8Q2NkM7hlh952qGx4ezX5q2sHehhpGiNBGV5mYiYfPky7tdtcVK89GuOexWs4dHu0uqtZXOoV1rXynGz7/m98j5slUgmZ7WUnhRZTotu2rDZlYsPrDsukp5xibHsiEEMDw+zOo7VzM2OdbgPVMUJZdKc9OLtlhpDto1h91qVmtUydCuta8U4+dgKGiMyR750j+33F2EXlSZ5dTYpdZVyhNLxrIhlGF4fJhYUgeXitJMVJqbXrTFSnPQrjnsVrOu2net0Y6jXWtfKcbPwdCIiGRlCSKyHBj1sf0ZwYsqs5wau9S6SnnCgTCD3YN50wa7BwkHWm6MrShtTaW56UVbrDQH7ZrDbjXrqn3XGu042rX2lWL8fHevAv5NRF4QkReAjwPn+9j+jOBFlem07PqVi9m4+cWy6yrl6evqY93J67JhlLlet6+rr8F7pihKLpXmphdtsdIctGsOu9Ws1qiSoV1rXynGN7V2tkGRHgBjzB6f2vsGcDqwzRhzdCXr1Kp9rcUmN3dWgJHxWFYXG50dZvtEPGtOGugOs3MyUVXbnajsbDKTS8OVr4rikRmr2VJ5lWuQCwcsAgKTCZuAJYSDFvGkwRjTsTnX7MxwDje8ZqemEozuzT+Oz5oVLDIhllJuu6HH9daiwtrXP2CLU7NNTkRWAt8yxthQPAgSkcOA/Y0xv6lyEzcAXwJurGU/veBFlZm7rJMp6fqVi/nRw1v4yj1/5m+Pms+/nLKIC3LmezHVdaKZzhKLaCTa6N1QFKUMbrnpmItnn0AoKFxzx9Oc+4ZD+PjGRzo655qdds1hp5qNx5M8PTqed5y+fuViFkW7GRodr8kyp8f11qNda1/Jx4+PdvqB34vIN0TkIhE5U0TeKyL/LiK/Aj4PvFxt48aYXwMtoe5wstBcsGEzZyw5CIAViw/MBmxmvhdTnZrpFEVpNRxz8eaHCFoBViw+MDsQyszTnFMaybY9U0XH6Qs2bGZ0b6xmy5we1xWlOal5MGSM+QJwAnALMACckn7+EnCOMWaFMWao1u2UQ0TOF5EHReTBkZGRem/OETdTUiD9ic+8SKhmU52a6dqDZqhXRfFCtTXrlouWeMtERfFKNTXrapPzwTKnx3VFaU58uejXGJM0xvzcGHO5MeaDxpiLjTFfMca84Ef7Fe7DV40xS4wxSwYGBmZqs3m4mZKSduq+rJ0T8ZpNdWqmaw+aoV4VxQvV1qxbLtrGWyYqileqqVlXm5wPljk9ritKc6KuSB9xstBcv3Ixtz6YGhNu3Pwi1xfM92KqUzOdoiithmMunn0CCTvJxs0v8rkVx2rOKU3D/J5ZRcfp61cuJjo7XLNlTo/ritKc+G6TqwcicjDwo2awyZWzycRiiTybXH8kzPYJd7tc7vqFbXsxz1XwomDvCCRiEAzD7AGwpve70Jgyb9Y8dk7tbBaLW7OgNjml1WiKms1kWyJpEwxYhAOCbQzxpEEEjAHbGCwRZs+y2DtlEw4G6I2E2DER7yzzVpmsrqrJ1sr3htfs5GQi77jdHwnT1RUkHk+m6jg9fX7PLETEk2GuI2xydahhP3EzxNVgTWyzP2DnUbNNrt6IyC3AMiAqIluAy4wxX6/X9krZXmzbFFmRcm0yiYTN0yPTthkne9z6lYtZ98unueOJbR7arvH7DWwbtj0B334P7HwB5h0E774F5h8FVioAhnYMsfrO1QyPDzPYPcjak9ay/uH13LXlrqxbf2HvwmY6YCqK0iIEgxaD8yJZs9wPH97C2447gAtvfiibd9edfQK3/+Ellh25L5//6VMMzAmz+pRFeZnY9uatMlldVZOa756YnEwwtL3YJrewv5vnduzN6xvc8oG/ZtdEwpNhzouttiWpQw37unsO74d1J6/jsHmH8czOZ4qm6/uiM/DtLywis0TkH0Xk30Tk05lHre0aY95jjNnfGBMyxiyo50AISttenKxIuTaZwvlO9rhVGzazYvGBntuuib0j08EEqf+//Z7UdGBsciwbAADD48OsuWsNyxcuzz5ffedqxiZbQuqnKEqTksm5M5YclB0IQSrvLrz5Ic5YchAfvfURVi07jBWLDyzKxLY3b5XJ6mrQfPfG9omYo01u+0Rx32AqYep33G5V6lDDfuL0flh952pGJ0Ydp+v7ojPw88zQJmAXsBlo2SQoZXspZ5MptCa5mZLmRUKe266JRGw6mDLsfCE1HYglY9kAyDA8Pszc8Ny857FkG3dCFEWpO5mMDFjiat7MzciOM2+Vyepq0Hz3hhebnCXONerLcbtVqUMN+4nb+yFuxx2n6/uiM/Dz3N8CY8xZxpjPG2Ouzjx8bH9GKGV7cbMiZWwyhfMtlTySAAAgAElEQVTdTEk7J+Ke266JYDh1qjqXeQelpgPhQJjB7sG82YPdg+yK7cp7Hg7oTZ6KolRPJueStnE1b2YysiNNc2Wyuho0373hxSZnG+p33G5V6lDDfuL2fghZIcfp+r7oDPx8x94nIsf42F5DKGV7cbIi5dpkCuc72ePWr1zMxs0vem67JmYPpK7ZzQRU5hre2SnVaF9XH+tOXpcNgsw15ZuGNmWfrzt5HX1dfbXvi6IoHUsm52598AWuO/uEvLy77uwTuPXBF7jqjGNZf/czbNz8YlEmtr15q0xWV4Pmuzf6I2FHm1x/pLhvMCso9Ttutyp1qGE/cXo/rDt5HdFI1HG6vi86g5ptciLyKGBIXXK3EHiW1GVyAhhjzLG17qRXZsIml7EiFZpjCm0zGXtcZvlShrhybdeER5vc3NA+bJ/cTtxOELKCRLuiBIOhEhuYJmEnGJ0YJZ6MEwqEiEaiBK2md3VUQsMtR4rikaar2UxGJm1DwJKsTS4UEJIGAiJMxJP+GzVbhcKsjvTDxPaazFylbHJdgS5idqwor2swa9VKw2vWzSbn1DewbeN43O4Ia5wbyQTs+Qsk4xAIQc9+EKhfH8BrrXqxydnGrqQ/0yF/2PbFj+o83Yc2mopStpeMFckJ2zb8aXQ8zzbjZo9zCsVSbdeMZUHPvu6zxSIaiaZeRzLB0I6nWX33mmmryrK1LOxdhFUm0BJ2gqd3PM2au9bkmYsW9S5qlwGRoihV4pSRmUwEXE2eHdOJhPys9snMlZvvGaKRqGteL5y3kGd3PduRZi3bNkXWuNw6LOwbWJYUHbdLWWnbvpZtG0aenDGbnJsdrlStOr0fnKZrf6ZzqLkyjTHPG2OeB67M/Jw7rfZdbB2cTHRu9rhmZmxiJDsQgrRV5e41jE2Ut8GMToxmgyOz7pq71jA6MVrXfVYUpfkpZessNa9jqbOZq1Red6pZy4867OhanmGbnJsdzo9a1f5M5+Dn0PY1uU9EJAAs9rH9psfNROdkj2tmYnbC2apiJ8quG086G1nidtxlDUVROoVSts7Mz27zOpI6m7nc8jrhdgzoALNWuRqdqTZalhm2ybnZ4fyoVe3PdA41nxkSkU+IyG7gWBF5Jf3YDWwjpdvuGNxMdE72uGYmbAWdrSoVnBYOBZyNLCGrsvuNFEVpX0rZOkvN61jqbOZyy+ug2zGgA8xaftRhR9fyDNvk3OxwftSq9mc6Bz8uk/uMMWYOcJUxZp/0Y44xpt8Y8wkf9rFlcDLRudnjmpm+yADrlq3Nt6osW0tfpLwNJhqJsvaktUXmIqfrcxUF4JhvHlPRQ2l9Stk6S83rWOps5iqV151q1vKjDju6lmfYJudmh/OjVrU/0zn4YZM7odR8Y8xDNW2gCspZY8pZXgrnz+sKMjIeI560CZWxvBUa4RpmQypjjyu7ejLB2MQIMTtBV2AWtrGJ2XHCVpB5XVF2xne5mluyNjk7TsgK0d/Vz67YLkeTUdgK02fAik9UZLmbQaOREw23HLUjlQ50Hj330TrvSVvS0JrNZKlt2yRsQ9I2dM8KMJUwjhkZCloELWEi1oEGrgyF2d3VB+Mvp8xcofTZhmS8KC9LGeNKZWc8GWd0YpSEnSBoBenv6ueV+CvEkjEkXT4iMpNW0IbnrJtNzgttZZNz6094ne7UdE5fI2wF6YsMlBQ1OfUJAE/9hMKaj0aihALOZ3sK+zNqk2tP/Ei2zBerdgFLgD+QKoxjgQeBpT5swzfKWV6c5l+/cjFfzDHCrV+5mCP3nVM0ILJtw9DInsYbZHwwEFmBINGe/R3NcmtPWsv6h9dz15a7HM0tQSvIft37pXbFwfRStP7SK1j4k09i7dmWt5/VWGIURWkOMlm69udPce4bDuHjGx9hoGcWHzv1CD566yNVGTfbHqfsPvMm+NXnUwOiUy6HTRcW5bot5GXlSQtOYtXxq/IsWE7ZaRs7zxrntN4VJ17Bt574Fhe99qKOyN6pqQRD28e5YMPmvD7Aomg3s2ZV3mUqZaVtKdz6EwNHlrbGlbDXZpv2aK4t1Seo9GxNPBlnaOeQo0HRaUCU259R2hc/LpM7yRhzErAVOMEYs8QYsxh4LfBSre37TTnLi9P8CwqMcKs2bGbbninPbc8YPtpcnMxya+5aw/KFy7PPS5lbnEwvRevffxljb7q0aD/raYlRFKW+ZPJwxeID+fjG1OBn1bLDsgMhaF3jZt1wyu7vngPHvwdOvHh6IJSZl87LwqxcvnB5kQXLKTsrWe+yey9j+cLlHZO9o3tj2YEQTPcBRvdqTQLTdbfnLzX3M7yaa/3oE6ghTnHCz3PeRxhjstexGGMeE5FX+9i+L5SzvFRqhEskbc9tzxg+2lzczHJzw3PznruZW9xML0Xrz+4r2s96WmIURakvmTycFwllczH35wytaNysG27ZHemd/rlwXiJGjEBeVs4Nz60oOwsz1m29zPROyN6EbRxrNGHXdktBy+JWk8l4zf0Mr+ZaP/oEbqbERAW2XKV98fN89yMi8v9EZFn68TXgER/b94VylpdKjXDBQPGvrmkMMj7aXNzMcrtiu/Keu5lb3EwvRevvHSvaz3paYhRFqS+ZPNw5Ec/mYu7PGVrRuFk33LJ7Ykfq4ZLrhVm5K7arouysdL3M9E7I3qAljjUa7MTLNsG9JgOhmvsZXs21fvQJ3EyJ+iWqnY2fg6H3AY8DH04/nkhPayrKWV6c5l9fYIRbv3Ix83uKrwVuGoOMjzYXJ7Pc2pPWsmloU/Z5KXOLk+mlaP2lV9D366uL9rOelhilvTn4X2/39FD8J5OHGze/yOdWHJvKzruf4aozjm1542bdcMruM2+Ch2+Be6+F5dc55nphVm4a2lRkwXLKzkrWu+LEK9g0tKljsjc6O8z1KxcX9QGis7Umgem669mv5n6GV3OtH30CNcQpTtRsk2tG6mWTy9iPStnkmsYgU6NNLq+pAttLOZtc0fqlLEdqk1ObXB1scl4HOH/+7Ns8Ld9CNJ1NriuUOusTT9qEgwF6IyF2TMQbn5nNQmF2R/phYnvqeSgCdhKSxblerU2u1HqWWFhYWJY1k9nb8JydmkowunfaJhedHfYkT2g7fLDGuTbtg03Oa116sclVSAcHVntQ87tbRL5rjDlTRB4FikZWxphja92G33i1vAQCFoPzUp8SVaLdbgqDTIU2l4rbCoQAA4EQViBANOj+KYqdiKfCzSQISyrccj91SSTiJJJxknaSBHHsrihWt3N7llj6iY2itCilsjYeT7JtzxQv7UoQtISADoCcO5V2zv2pJgnd6U/N947AKy9NL1/w68vNztwOpCUWIQkRN3FsYzt2KDslc90+vCwcjHf04LwUxk7dO2QnICmp514vOCroX5QbTDn1CbwOkAJWgFAghMEQCoQIWIGq2lHaBz8+6vhw+v/TfWir4ZRSbwN58/72qPn8yymL8hScbtrtVsWr3tpOxBna+TSr774kR5V5DQvnLcIKhkgk4jy9q1hruWjuQoJB/VZnRekE4vEkT27bk5edn1txLN+87znWvPWIzlJrl/oqBHCeF+yCDe/ITrNX/oAhK+mY01Cs3f7gcR/kktyM7sCvLHA71h/WP5unRorV2kfO7yEU6sB72dzqM3oEbHs8ZTvM1cDvezSUOLOT17QPX5/huY/isvxh8w7jmZ3P6Fd5dCh+qLW3pn98CxA2xjyf+6i1fQAROVVEnhKRP4nIv/rRphul9NiF81YsPrBIwemm3Z4pjG0Y3zXF7u0TjO+awtRowPGqskypMi8pUGVeklVljk66aC0nVWupKJ3Ctj1TRdn58Y2PsGLxgR2h1s7P6b2Yuz7rrCh20xrveDZv2tiu511z2kmffUlhRneINjsXt2P9yLizWruRx/WGUkqtnRkIZaZ/95zU9Aop1b+otC/juY/isvzoxKh+lUcH4+dFsAcBXxGRg4HNwK+Be4wxD9fSqIgEgC8DbwW2AA+IyG3GmCdq211nyumxc+e5aWKdtNszgbEN24f38OPrH2X39knm9Hdx2gXH0D/Yg1T5KatXlWXMuKgyTUpbGXfRWsZVa6koHYObvjiTqe2s1nbM6XOvpH/PX5CXHkgtlKsodtIXh2bnTYrN6i6Z09Vot9sdt2O9qrULcFNr2wl35XaFuPUvbNuuuC/juY/isnzcjuv7ooPx7dyfMeYyY8zJwGuAe4CPkhoU1cpfAX8yxjxrjIkB3waW+9CuI6X02IXz3DSxTtrtmWDv7lg2PAB2b5/kx9c/yt7d1b+Zvaosw+KiypTUuDvkorUMqdZSUToGN31xJlPbWa3tmNPfHGbvGy6bXiijKHbTGsf35k0KT4275nS12u12x+1Yr2rtAtxq0Aq6K7crxK1/MSvWXXFfxnMfxWX5kBXS90UH41uvXUQ+JSI/Ae4ADgc+AizwoekDgBdznm9JTyvc/vki8qCIPDgyUvk3IBdSSo9dOG/j5heLFJxu2u2ZwE7Y2fDIsHv7JHai+k+0vKosU6rMawpUmddkVZnRLhetZVdn3LCbwa96VZSZws+and8zqyg7P7fiWDZufrHt1dquOT073RHLVRS7aY17D82b1jf3Va457aTPvqYwo9tUm12qZt2O9QPdzmrtRh3XG04ptfaZNxVr4Hv2q7hpt/6F2FbFfRnPfRSX5aORqH6VRwfjm1pbRB4CEsDtwK+A+40xNV9kKyJnAKcaY/45/fwc4K+NMR9yW6dQoVlojCmnci2lx84YkDLKzf5ImO0T0wrOge4wu6aS1Wliy2kqk4nU9bjJeOrTl+59YXIsu/x4ch4bP785L0Tm9Hex/CPH0j1nVp4Oe15oLjsnRyvSWRYaVvYJ7cP2ye3TWsqufkKTO7L7keiay+jkGHE7ScgK0N/Vx674nuz6c9Prx+0EIStItCualSdUq4etZL9raStNw5Wv7YiqtetKQ2q2VOZ2hSymEoZE0iZgSdbQK5La3VJfXdDSpPN9PDabjVc/WpTTKy49hm5GU5fA2QnAgDGp/8UCCWAHQowJxOwYFoIFWMbQZwQ7OItRO0bCJAhI6syaiBCNRLHEymahIAQkQNIkMZhsFtrGZnRilHgyTtAKErbCGDHZzmClli0fjFwzVrOFx/P5PbMIhQJMTibyjuv9kTBdXcHm+eoMP/Ciy7aTqb6HnUidEerZD4IhSEzBnm050+dDcJZ7DTi0PWXHGZscy/Yn+rr6SIzj2JdZ8bHFRPYJFbWdSCby+iT9Xf2Eg2HX/XBTa9dQuy1aBEoG365NMsacICL7ACeSur/nqyKyzRjzxhqbfgk4MOf5gvS0iig0xvztUfNZfcoiVuWYYjK2uEyouelgEwmbp7btyVv3+pWL+dHDW/jKPX+uqO0SO+puFLKs1EDo5cemb1g84m3w5o/lmVxmn/0DTrvgmLzrbJe+70A+/dAn+eDxH2T9w+u5a8td2bMxuc/XLVvLwt5FjgOiXJVlPBlnaGexDW7h0D2Efvox7KWreWbxu/NsckXbcjG0FFpeTlpwEquOX5W3rUrtLn62pShK5ZTK3IGeWXzs1CP46K2P5GXoF3/5NHc8sc1bZrYSOfk++5A3c9r5/5cff/WP0/dDfPAoZj9wFbx4L5xyOfz2evjrD8JtHyppjLvixCv41hPfYs3R/0TMjrP63k8WzVt1/CoW9S5i3qx5PL3j6WKTZ+8ibGMXzbtm2TX89Nmf8s4j3kksGavIsuWHHWymcDqer1+5mIXRboa2F9vkjhjo5pntex1Nsy1Xq279jYEjYeTJ/OkrfwDxcfjOynxr3PxXw7Y/Ftnk7PlHMfTKc8U1MPcwrIK2Y//0S56JjRbV5OH7HF7UlzntgmPo6gk61lfQCnLhLy7Mb2Pu4TznsB+Hzj3UsQ+zqHdRdmCkdB5+XiZ3NHA2cC5wFqkBy50+NP0AsFBEDhGRMPBu4LZKV3YywK0qMMVUai/atmeqaN0LNmzmjCUH1dy2q7Flb/rUfqG55fj3FJlc5OZ30N8bY/lHjuVvP3koh78vyOVPfYI7t9zJmrvWsHxh6larjMEt9/nqu9dkjW+lGJ1wscG9JvXJ+tjr3ltkkyvalouhxcl6VLitSu0ufralKErllMrcVcsOyw6EYDpDVyw+MPu8LW1yOfkui/6O/vv+hRX/mOS9Hz2AFf+YpP/eDyEHvQ5OvBg2XZjK98xACFyNcZfdexnLFy5nS/yV7ECocN6au9YwOjHqnt0u8y65+xL+YdE/sGX3lootW17NXo3E6Xi+asNmRvc62+RGSphmW45ShrjC6TuenR4IZaZ995zUGSEHm9yYm5Vtonib2yXhWJPbp7bTP9jDio8t5r3/8QZWfGwx/YM97IjtcGx7eM9wcRuT212tcW7vA6Vz8fOu9c+SMsitAx4wxlSuFCmBMSYhIh8CfgYEgG8YYx6vdP1CY4ybAa4Se1E8aTuum/mywFradjW2ZIxCyXj+/Eiv4/IS38t4YC/vvPPv82YNjw8zNzy35PNYBUa3hIsNLmFSBr2YFXScX7QtB0NLoeWlFuuRn20pilI5pTLXLSPnRUJ5z9vOJpeb75Fe5Kkf0v3UD/OXWboq9f/OFxzz3c0Yl8lWt3kZUxbGeZlS8wISIBKMVJydXs1ejcTteF7KJlf18b3ZcOtvFPYzIHXZpgebnKtR1mH5hJ107k/YCcQSuufmX6HjVl+RYKRommtfxdVo60uXVWlR/LTJnW6M+bwx5j6/BkI5bf/YGLPIGHOYMeY/vKxbqQGuEntRKGA5rptMKzdradvV2BJM30gcCOXPn9jhunzYxdi2K7ar5PNwBUa3oEvbwfQlEGE7UdG2nQwtflqP1KCkKI2hVOa6ZeTOiXje87azyeXmu1t2T+yYnuewjJsxbldsFxOJCdd5GVNWKOBsyyo1L2mSrm1XkuGllm00bsfzUja5qo/vzYZbf6OwnwEpc6EHm5yrUdZh+aAVcO5PuPRF3OprIjFRNM21r+JqtNUvfe9kmusi3jrgZIBbX2CKqdReNL9nVtG6169czK0PvlBz267GltkpC1uRueXhW4pNLunlU0a3YmPbpqFNrs/XLVubNb6VIhpxscE9nrphve+BG4tsckXbcjG0OFmPCrdVqd3Fz7YURamcUpm7/u5nuOqMY4sydOPmF7PP29Iml5vv914Ly68rtnA9fMv0vIdvgbd/qawx7ooTr2DT0CYWhPZh3Yn/4Thv7UlriUai7tntMu+aZdfwP0//DwvmLKjYsuXV7NVInI7n61cuJjrb2SY3UMI023KUMsQVTu89FM7a4GCNm+9ok+tzs7JFirfZb4KONdnf1e+42271Ndgz6NiGmzXO7X2gdC6+2eSaiXI2uXldQUbGYySSNsGA5clelEjYKftMet2B7jA7JxMVm+pKUs4ml4gXGF3mw/jItF2uZz9ICxDsZIKxiZGsLW5eVz87J7fnPO9j58R2YiZBWIL0RaJYOUY4O9LPWCxtXrPC9Bmw4hMQDBPv6mU0zybXRyhnP+zu+YzlbSuaZ7LzYiIqNMB5McKpTa41UJtcXWlOm1zcJp42dUVnh3klVqWBs9GUy2y3ZcPdEBtPKfSMAZMECaRyPTirYJ6dsslZwXybnFhYWFgmSZ8N5MwThNS/lE0u80l7wk6kjHF2nKDkG+OyNjmHeUDFWdpKNrnC43mmLxCLJVJ9hBxLbDjcITa5xCTsGcnpZwwAARebXAz2vJwzfd9U/8GDTW7SjrFjcke2P9Hb1UtXsMt9tx3aTtpJT3a43PdByArlvUeqpEWLQMnQEd90mWuHKzQdeTXCBIMWg/PyT5UPhPJPkzuZ6Crc0VSYOGHbMPrU9A2IDja5XPucFQgS7dl/et1tTxDNNcSceRPRX30enrq9qC37yNMZessnWX13jnlt6RUs/MknsfZsI/TuW9g/Y7lzsNJY776FaGZ+mmiwsk9dcs112XXTz72aikq1pShK/XAycg7MmVVz/jYV5QyghWTyPbPeXf9ZZIzjrA0Q6oYN75ie9q4b4ddXwVO3Y807iGiJbZRLt6AVZP7s+a45ul+3+3fEVJrDTrnbrDgdz23blLTGVX18bzac+huJKdj2ZJEhjjmDcMNpxea50acd69+yXGqgYJsJO8Gzu551Nbs57rZDfVkBi/0z/Z0yy0LqfVCq1pXOo+bL5ETkhyJym9vDj530k0LTUcsYYQrtLw42uTz7XKl1MzaY49/j2NbYCWdnB0KQtrDcfxljb7q0eDvlLHg+0kqmok7gmG8eU/ahKLm0bP46UW32ZdZzMMbxnZUpe1futO+9dzqrfcjXWnO03XO4rWrUKy6GOJKTlZnnPNammt2UZsGPM0P/5UMbM0ah6QhaxAhTaH9xscll7XOl1s0sG+l1bCs2u8/ZBjO7r3g75Sx4PtJKpiJFUYpp2fx1otrsy6znluGh2cXTMlld6TZKUGuOtnsOt1WNesXFEIedLJ7mZJ7zWJvxZFzNbkpTUPOZIWPMr0o9/NhJPyk0HUGLGGEK7S8lbHJl180sO7HDsa3w3jFnG8zeseLtlLPg+UgrmYoURSmmZfPXiWqzL7OeW4bH9xZPy2R1pdsoQa052u453FY16hUXQxxWoHiak3nOY22WMhwqykzi55euLhSRW0XkCRF5NvPwq32/KDQdtYwRptD+UsImV3bdXHuRQ1t9D91cZKNbt/QK+n59dfF2ylnwfKSVTEWKohTTsvnrRLXZl1nPwRjHWRtS9q7cae+6cTqrfcjXWnO03XO4rWrUKy6GOAJdlZnnPNammt2UZsE3m5yI/Aa4DFgL/D3wPsAyxnzalw14wKvpqFWMMIWGuL6uPqw927ImF7tnX8birzhbfAotLl29eRYYu2d+yrqSbntuVz/bp8aIJ+OEAiECWEwmpwhZQaJd/QRzzHNE+mFiu7tRyYtxqdzvoAZDXCtZjlqBSu4JqsQApza5utJ0Nduq+etIJdmWWca2U9Y4YyAQTn3anoynMtgkpy1dMG3ukgCEIhCfADuBPauHMZMgZiewrCCWBLAsK98Gl87sUoaseDLuaN9yfZlV5m6Vmdvwmm2rGnXDi01OgqmaLDDX2ol4qk+StdIOYAW9ndVxq8WyJricOrfEclzWa/3V0Edos+LoPPy0yUWMMb8UETHGPA9cLiKbgRkfDJWjFY0wtrEZ2vVMvsFn2TUs/MV/Yj35o7QB7t9Yffclzqa1XItLMgEvP1Zgj5te96QFJ7Hq+FV5hpcrT7ySax+6ltGJ0ZTtZfO3Cd73hfIGJa/GpTLk2mG82OW8muiUGeLyuXDIQeWXU9qGVsxfV0oZQKG0Oc4pBwvz8oi3wZs+Ct97L/Yhb2LoxAtYffel2Qy74sQr+NYT32LNkjVMJia5+K6Ly1q5EnaCoZ1DFRu8qs3OVs7ctqpRJ9yOy9FFDja5DRCeDRvembesHT2CoZ1D+dbZZWtZ2LsIK1BZ19I2Ns/ueraoRg6bdxjP7HymaPqhcw8tqt3r3nIdCTtRcRtu9dfK9arUjp9/4SkRsYAhEfmQiLwD6PGx/Y7G0eBz9yWMnXB2av4JZ2cHM9n5boafPX9xsMdNr7t84fIiw8un7v0U7z/m/dO2lyXvTbVVziBTR9ucF6tRuxuQFEVpQkqZ45xy0Mka+r33ws4XGPubi7MDIUhl2GX3XsbyhcvZsntLdiCUmedm5fJq8Ko2OzVzmxi34/Kelx1scithx3NFy45NjBRbZ+9ew9hE5cd2txoZnRh1nV5Yu8N7hj214VZ/Wq+djZ+DoQ8Ds4HVwGLgHOBcH9vvaFwNPmnDm6sBzsnwU2CBKVx3bniuY1tzw3OzP8cDOTdUljLI1NE258Vq1O4GJEVRmpBy5rjCHCxhDY1ZQddcjgQjFVu5vBq8qs1Ozdwmxu247GaTczAcxkzC+e9rJyreDbcacavRhF28Tdfad2nDrf60Xjsb3wZDxpgHjDF7gFeA1caYdxpjfutX+52Oq8EnbXhzNcA5GX4KLDCF6+6K7XJsa1dsV/bnUDJHtVnKIFNH25wXq1G7G5AURWlCypnjCnOwhDU0bCdcc3kiMVGxlcurwava7NTMbWLcjstuNjkHw2FYgs5/X5f71JxwqxG3Gg1axdt0rX2XNtzqT+u1s/HTJrdERB4FHgEeFZE/iMhiv9rvdBwNPsuuoe+hm1PzH7qZdcuuqczw07Ofgz1uet1NQ5uKDC9Xnngl33j0G9O2lwdvTLVVziBTR9ucF6tRuxuQFEVpQkqZ45xy0Mka+q4bYd5B9N1zLeuWXZ2XYVeceAWbhjaxYM4Crj3p2oqsXF4NXtVmp2ZuE+N2XO7Z18EmtwF6Dylati8yUGydXbaWvkjlx3a3GolGoq7TC2t3sGfQUxtu9af12tn4aZN7BLjIGHNP+vkbgeuMMcf6sgEPNLWdqwazWrFNrh9rfFvW8BLvjjI6OUbCThK0AkS7+gkFZzlvt6sPxl/Ormt378tYfFfWojI3PJftk9uJ23FCVggLi6nkVMr20tVPKNcmV+41+GiTK2rag/1FbXL+4otN7vK5HFOhQEFtclWhNdsokom0gSuRMscFZ0FiKm2OC6WexyeKMzE3L0VS5rlkDIzBDnczZk8RM0lEUpcqiwjRSBTbTrJ9cixr5err6mV3fI9j3mWNXOl8L2Weg+qzs1Vtci2Ll2Nttj7zDXEkYnmmWXr2BbGcbXKFfZLIQEqe4GE/3GoxloixfXJ7tp77u/oJB8OOy6tNTqkVP21yycxACMAY8xsRqfzi0U6gFrOabWONPEnUZd1EIsbQrj8VG4LmHp46yJXZrgVEg+6WtlybnGfDSjnjUg3k2uX8XFapH7kDlT93NXBHFKVeFBg7s9/X8qvPpz6EOuVy2HShcx6XyEsL6HPI5/VvXe9ok1v/8Hru2nJXkRkraAXZr3u/il9OtdmpmTuDeOlf2DaMPGjVKzcAACAASURBVFm87MCRMPp08fRgF2x4R1G7ViBItGf/qvfDNraj8e2QfQ7hTw79mYXzFhIKhBxr16nOvNaf1mvn4qdA4Vci8hURWSYibxaR64C7ReQEETnBx+20LrWY1cqsOzrpYgiaHPW8XSerSq5NTg0riqIoJSgwdqasXOek7HAnXjw9EMrM82DYdMpnN5vc8oXLs881t9scL8d5V5vcX5yn73i28nr1sB9uBrftk9s9GQ8VpVb8PDN0XPr/ywqmvxYwwMk+bqs1qcWsVmbduJ10MQQlwU562q6bVSXXJqeGFUVRFBcKjJ3AtFUu83PhvAoNm0757GbUymR25rnmdhvjpX/htqxb3TrY5PwwyLr1NZyscZnpilIP/LTJnVTioQMhqM2sVmbdkBVwMQQFPG/XzaqSa5NTw4qiKIoLBcZOYNoqV6lZzgWnfHYzamUyO/Ncc7uN8XKcd1vWrW4dbHJ+GGTd+hpO1rjMdEWpB37a5PYVka+LyE/Sz48SkX+qsc13icjjImKLyBJ/9rSB1GJWK7NutMvFENQV9bxdJ6tKrk1ODSuKoiglKDB2Zu8ZevgWuPdaWH5d1YZNp3x2s8ltGtqUfa653eZ4Oc672uT2c57ee2jl9ephP9wMbv1d/Z6Mh4pSK37a5H4C/DfwSWPMcSISBH5vjCmvnHJv89WADXwF+IgxpiIVTFNbY2oxq5VZN5GIMTo5StxOErICRLuiBDOfxnjcbp5VxQpjWRaTiclqLWztglqOcqjWJpcvUPhHtcnVF63ZRlFo6+reFybHUhkciqQuX05WZ9h0sl7ZyWQ6/xOE0vatXfFXarFnNgqt2Wrxcpx3W9ZpOnjrt3jYDzeDWzwZZ3RiNGuTi0aihALO34XVBKhNrsXx85xj1BjzXRH5BIAxJiEiyXIrlcIY80dIqUPbBi9mNadAyV3XtlMKzPT8YKSf/QikDrJWIKXFdNmubWzGJkZdD5TtaFXxQa2tKEqnU2lHLxCEuQuml9/zl9Ty+xzgbPfKyfJynU2nfLaCFvsVmL0yhtCil9CgLNQMbiKMnRqo2wlISuo5zjZD29iMBQLECBAOBOiTMpcVeejnuPU1QoEQ+xea6nxC61ApxM/B0LiI9JOSJSAirwd2lV7FP0TkfOB8gIMOquyT5qamnJ6ycP4Rb4M3fyxf5VpCZ1moZvWsy24xmu01t129Km2P1izevx6hkuVr+cqFal5Cg7KwEdvtqJr1Ukdu6vd9j04N4nObbbJjZ6202+tR/MHPv/wlwG3AYSJyL3Aj8C/lVhKRX4jIYw6P5V42boz5qjFmiTFmycBA5d+A3LSU01MWzj/+PcUqV486y3bWrjbba267elXaHq1ZvH89QiXL1/KVC1XQqCxsxHY7qma91JGb+n3PX4oWbbZjZ6202+tR/MG3M0PGmIdE5M3AEaSun3zKGBOvYL23+LUPbUU5PWXh/EhvzTrLdtauduJrVhTFZ7x+PUIly9fylQtV0Kgs1AyuM17qyE2hnSzusrXb363dXo/iD74NhkTkXcBPjTGPi8ingBNE5EpjzEN+baOjyOgpcwMrV09ZOD+ja3VbPoeMzjI3ENpdu9qJr7ndqFbYoCi+US6Xq1nea5s10qgs1AyuM17qKKPQLlzWQVDQbn+3dns9ij/4eZnc/zHG7BaRNwKnAF8Hrq+lQRF5h4hsAZYCt4vIz3zYz9agnJ6ycP7DtxSrXD3qLNtZu9qJr1lRFJ/x+vUIlSxfy1cuVEGjslAzuM54qSM39XvPfkWLttvfrd1ej+IPfqq1f2+Mea2IfAZ41Bjzrcw0XzbggZlUaJa1ktSi0i6lZg2Gsbv6GJscJWYnCFtB+rqiWDnzq9FZZkjEY0zs3IGdTGIFAkTm9RIMVfbJSbOaWnzYL1W+5jDTau1KyGxP1dpZtGYLMLZNcmwME4sh4TCBvr7UL6lWJbHbcggkplLWrkAo1eEsuEk9t007FGFMIGaXyKn08gkJsnsygUkkkVCAOfv0EQyW1w87ZSFQ99yuMIO1ZqvFqTaNnd+PyNRfYf8iM92hDVtcaqOWr+woU3eJRJyJHWPTfZDeVG370b+oQx+ljZTHnYmfNrmXROQrwFuBz4nILPw989R0lLWS1GIJsm0YeTJ/3TNvgl99Hp66HfvI0xl6yydZffeaqowopdTZiXiMsRefZ9M1n+GVkW3sMzCf5Zd8gr4DX1V2QNTMppZ21IUrilI5xraZenqILRddSPylYUIHDLLgy19mVn8QufkdleV0JdrgTPbf9Z/w1x+E2z5Uuu10mxXlZ7rtxJ/vZWTfk/nR1dM5ffqln2DgwIPLDogKs3CmclszuM4U1mY5a9zcBfnru/RZrPlHFf/dPPZvnGps/VvXE0vGiurusDmHuPZBntn9XM11qnWoFOJn7/RM4GfA3xljdgJ9wEd9bL/pKGslqcUS5LTud89JWeOAsRPOzg6EHLddAxM7d2RDCOCVkW1suuYzTOzcUXZdNbUoitKsJMfGsgMhgPhLw2y56CKSW//sr80tk9/Hv2d6IFRB2xXlZ7rt3Ye/PTsQglRO/+jqz7D7Fe9Zq7ndpniwxgHe+iwe+zdONbZl9xbHuivVB9E6VeqBnza5vcD3c55vBbb61X4zUtZKUoslyG3dSG9q27P76mZEsZPJbAhleGVkG3ay/HfoqqlFUZRmxcRi2YFQhvhLw5hgd/6CtdrcMvntwfIJFeZnum2TNI45bRLev+tcc7tN8WCNA7z1WTz2b5xqLBKMONZdqT6I1qlSD/y8TK7jKGslqcUS5LbuROrsTHjvWN2MKFYgwD4D8/PCaJ+B+ViBQNl11dSi5HH53KJJf+6q3+a83iukdBYSDhM6YDBvQBQ6YBBJjOcvWKvNLZPfHiyfUGF+ptuWgDjmtATL53RV21VaDw/WOMBbn8Vj/8apxiYSE451V6oPonWq1IO2vqen3pS1ktRiCXJa98ybUtY4oO+hm1m3bG1djCiReb0sv+QT7DMwHyB7vW5kXm/ZddXUoihKsxLo62PBl68jdEAqnzL3DAX2P9hfm1smvx++Bd7+pYrbrig/023P+dNtnH5pfk6ffuknmLOP96zV3G5TPFjjAG99Fo/9G6caWzBngWPdleqDaJ0q9cA3m1wzMaM2uWSCsYmRaaNbZAAr1xRUi00uEU9d22snwAqmboyc3DFtk4v0MxbbWRf7Tzva5HxALUc5VGSTe+6Fssv4aZPb/cfPVrWe2uS80ao1C1XY5CrJ8NxlREACIBZYgdQlSSYJxlRt3Cprk0smkWDlNrmqtzszaM1Wi5MhzpiCfsR+UKpGvPRZWtQmVwfUJtfi6GVytWDbWCNPEi1lU6nEPOREMgHbHne3wJA6rVcvI0owFGbOQBX7jZpaFEVpXsSyCEYd8skppysxZjkt8/Yvwe++Aif9W2X20Bwqys/0cSUI9HaXXtTX7SrNi5M57qwNEOqGDRWaEsFbn8Vj/8atxpymBYMhxz6I1qlSDxo+nG5parHFlcOrBUZRFEXxl0oy3mmZ2z6UMsn5dTxQlHI49Rm+sxJ2PFufPoqitBF6ZqgWarHFlcOrBUZRnHAQGOTP3zUz+9Hk1Fu80MaX4bU3lWR8KfOnX8cDRSmHW58hNLt4mtakouShZ4ZqIWNTyaVWC1GGjAWmsG03C4yiKIriL5VkvNsyGZOcH8cDRSmHW58hvrd4mtakouShg6FaqMUWVw6vFhhFURTFXyrJeKdl3v6llEnOr+OBopTDqc9w1gboPbQ+fRSl4xGRH4vIvEbvhx/oZXKAbRu2j8eIJZKEgwH6u8NYVgVyEMtK3Yj4z7+YtqlE+qu3x+USCML818B5P863wARm6E9WiwVPURQlh6ozttFYFgwcCe/7Sb6hKzcLC48DGZvc36+d7nTueblyG53mbdPS1HXs1mewAvl9lHK15WSkm6l+h9JSGGNOa/Q++EXHV7htG556eTcfuPFBtuyYYEFvhK+9dwlH7Dun8gFRxqZSiXmo8h2D0af8acsrfr4ORVE6mpoztpHYNow8WT4L3axa1droNG+bjqav41J9hkqNb05GugKLrdJaiEg38F1gARAA/i/wufS0/w+YAP7RGPMnERkA1gOZ6y0vNsbcKyI9wBeBJYABrjDGbBSRPwNLjDGjIrISWA2Egd8BF6bb+HrOet8wxqyt92uuho5P2u3jsWy4AWzZMcEHbnyQ7eNV3GDop12unqa6Zt62oihtha8ZO9PUmoXV2ug0b5uOpq9jP+pILbbtyKnAsDHmOGPM0cBP09N3GWOOAb4EXJue9gVgrTHmdcAK4P+lp/+fzPLGmGOBO3M3ICKvBs4CTjTGHA8kgbOB44EDjDFHp7f133V7lTXS8UP9WCKZDbcMW3ZMEEskvTfmp12unqa6Zt62oihtha8ZO9PUmoW12Og0b5uKpq9jP+pILbbtyKPA1SLyOeBHxph7RATglvT8W4DM2Zq3AEel5wPskz4r9Bbg3ZmJxpgdBds4BVgMPJBeNwJsA34IHCoiXwRuB+7w96X5R8efGQoHAyzojeRNW9AbIRwMeG/MT7tcPU11zbxtRVHaCl8zdqapNQtrsdFp3jYVTV/HftSRWmzbDmPM08AJpAZFV4rIpzOzchdL/28BrzfGHJ9+HGCM2VPBZgT4Zs56RxhjLk8Pmo4D7gZWMX2mqeno+MFQf3eYr713STbkMtcB93dXcSDy0y5XT1NdM29bUZS2wteMnWlqzcJqbXSat01H09exH3WkFtu2Q0QGgb3GmA3AVaQGRpC6rC3z//3pn+8A/iVn3ePTP/4cuChnem/BZn4JnCEi89Pz+0TkVSISBSxjzEbgUznbbjo6/jI5yxKO2HcOP7jwxNoNMU52uWqtQH62BdjJBGMTI8TsBGErSF9kACv3hshCm9HAkb5tW1GUzsXXjJ1pas3hamx0gXDKAPbKS2W3ZxubsckxYskY4UCYvq4+LNGcrgdNX8eV1Fo5AsGULKGwDRd5Qtl+hdIMHANcJSI2EAcuAG4FekXkEWAKeE962dXAl9PTg8CvSZ3RuTI9/TFS9wNdAXw/swFjzBMi8ingDhGx0tu5iJSc4b/T0wA+UddXWgNNXbUichXw90AMeAZ4nzFmp9/bsSxhYM4svxqr3NwyQ23ZyQRDO55m9d1rGB4fZrB7kHXL1rKwd1EquNRmpChKHfE1Y2eaWnLYq43OQxbbxmZoxxCr71w9nesnr2Nh70IdENWJpq7jSmutHIEgzF1QfnPl+hVKU2CM+Rnws9xp6ft6rjLGfLxg2VGmzxjlTt8DnOsw/eCcn78DfMdhF5r2bFAuzZ6YPweOTtsrnqaJR5XNzNjESDawAIbHh1l99xrGJtKWGbUZdRTHHHLQ9OObxzg+uHxu6qEoSvV4zVYPy49NjmUHQpDO9TtXMzY5Vo9XojQ7M3wcL9uvUJQWoqkHQ8aYO4wxifTT35LypCseidmJbGBlGB4fJmanf7VqM1IURfEfr9nqYflYMuac60nN7Y5kho/jZfsVStNijDk4fRZISdPUg6EC3g/8xG2miJwvIg+KyIMjI/rJRC5hK8hg92DetMHuQcJW+lS22oxmHK1XpdXQmq0Cr9nqYflwIOyc6wHN7QwdVbMzfBwv269QlBai4YMhEfmFiDzm8Fies8wngQRws1s7xpivGmOWGGOWDAyohSeXvsgA65atzQZX5trevkj696Q2oxlH61VpNbRmq8BrtnpYvq+rj3Unr8vP9ZPX0dfVV49X0pJ0VM3O8HG8bL9CUVqIhg/hjTFvKTVfRM4DTgdOMcaYUssqzliBIAt7F3HzqTc4W198NtcpiqIoeM9WD8tbYrGwdyE3v+1mtckpM34cL9uvUJQWoqmrVkROBT4GvNkYs7fR+9PKWIEg0Z79SyzgowVPaR9KSRQOOch9nqIoKbxmq4flLbGIRqJV7pjSdszwcbxsv0JRWoRm/wjpS8Ac4Oci8rCIrG/0DimKoiiKoihKuyAi56W/oLUjaeozQ8aYwxu9D4qiKIqiKIrSxpwHPAYMl1muLWn2M0OKoiiKoiiK0tZMJZJLX9oxcd/z28efe2nHxH1TieTSWtoTkW4RuV1E/pAWk50lIotF5FcisllEfiYi+4vIGcAS4Ob0VVgRETlFRH4vIo+KyDdEZFa6zc+KyBMi8oiI/Fd62t+LyO/Sy/9CRFrunoumPjOkKIqiKIqiKO3MVCK59OmX99x2wYbN0S07JljQGzn4+pWLb1u0b8/bZwUD91fZ7KnAsDHmbQAiMpfUV9QsN8aMiMhZwH8YY94vIh8CPmKMeVBEuoAbSInLnhaRG4ELROQm4B3AkcYYIyLz0tv5DfD69LR/JnWv/6XV/i4agZ4ZUhRFURRFUZQGMbo7dnVmIASwZccEF2zYHB3dHbu6hmYfBd4qIp8Tkb8BDgSOJn0fPvApYIHDekcAzxljnk4//ybwJmAXMAl8XUTeCWTEZguAn4nIo8BHgdfUsM8NQc8M+Y1tw94RVVQrM0Mp21sNHOOTKc6vdiplzqv/tewyu//42RlrJ8PB/3p7xctWw58/+7a6tq/koBmvNCtamy1Lwrb3zwyEMmzZMUHCtqvW9aXP6pwAnAZcCdwJPG6MqeryO2NMQkT+CjgFOAP4EHAy8EXgGmPMbSKyDLi82n1uFDoY8hPbhm1PwLffAztfmP7Ss/lHaSApiqK0OprxSrOitdnSBC1r64LeyMG5A6IFvRGClrW12jbTdrgxY8wGEdkJXAgMiMhSY8z9IhICFhljHgd2k7I3AzwFHCwihxtj/gScA/xKRHqA2caYH4vIvcCz6eXnAi+lfz632v1tJPoO8ZO9I9NBBKn/v/2e1HRFURSltdGMV5oVrc2WJjonfOn1KxePLuiNAKmB0PUrF49G54RruffmGOB/05fEXQZ8mtQZnc+JyB+Ah4E3pJe9AVifXlaA9wHfS1/6ZgPrSQ2WfiQij5C6T+iS9LqXp5fdDIzWsL8NQ88M+UkiNh1EGXa+kJquKIqitDaa8UqzorXZ0swKBu5ftG/P279z/tKrE7a9f9CytkbnhC+tQZ6AMeZnwM8cZr3JYdmNwMacSb8EXluw2FbgrxzW3QRsqnY/mwEdDPlJMJw6NZ0bSPMOSk1XFEVRWhvNeKVZ0dpseWYFA/cf0Bt5Q/klFb/RwZCfzB5IXaNbeM3u7IFG75nSqngUJPghLDjvx68C4IbTnq+5rWpYdf8XAFi/9MN1ab8SOYKiOKIZrzQrWpuKUjU6GPITy0rdrPjPv1Cbi6IoSruhGa80K1qbilI1OhjyG8uCnpb78l1FURSlEjTjlWZFa1NRqkI/MlAURVEURVEUpSPRwZCiKIqiKIqiKB2JGGMavQ++IyIjwPNAlMY6zxu5fX3t/jNqjDnV70Zz6rVWGv0394t2eB3N8hqavWa90iy/11LoPtZGO9RsM/9+C9F9rZ261GwzIiL/DvzaGPMLj+stAz5ijDm9LjtWI215z5AxZgBARB40xixp1H40cvv62hv32r2SqddaabXX7UY7vI52eA2l8KtmvdIKv1fdx+ZkJmu2lX6/uq9KISIipE6W2IXzjDGfnqF9CBpjEjOxLdDL5BRFURRFURSlsSSmlrLzxfsYe+45dr54H4mppbU0JyKfFZGLcp5fLiIfEZGPisgDIvKIiFyRnnewiDwlIjcCjwEHisgNIvKYiDwqImvSy90gImekf36diNwnIn8Qkf8VkTki0iUi/51e5/cicpLDfvWJyP+kt/9bETk2Z/9uEpF7gZtqee1eacszQ4qiKIqiKIrSEiSmlrLtj7fx3XOi6e+JOpgzb7qN+a9+O8FZ91fZ6neAa4Evp5+fCXwOOBH4K0CA20TkTcALwELgXGPMb0VkMXCAMeZoABGZl9uwiITT7Z9ljHlARPYBJoAPA8YYc4yIHAncISKLCvbrCuD3xph/EJGTgRuB49PzjgLeaIyZqPI1V0W7nxn6agdvX19759Eur7sdXkc7vIZmpBV+r7qPSiv9fnVfm4E9267ODoQg9cW53z0nyp5tV1fbpDHm98B8ERkUkeOAHcAxwN8CvwceAo4kNQgCeN4Y89v0z88Ch4rIF0XkVOCVguaPALYaYx5Ib+uV9GVtbwQ2pKc9Seo+vcLB0BtJn/kxxtwJ9KcHUwC3zfRACNr8zJAxpqFvnEZuX19759Eur7sdXkc7vIZmpBV+r7qPSiv9fnVfmwQ7sX92IJRh5wup6bXxPeAMYD9SZ3JeBXzGGPOV3IVE5GBgPPPcGLMjPYD6O2AVqbNK769xXyphvPwi/tPuZ4YURVEURVEUpXmxgluZd1D+tHkHpabXxneAd5MaEH0P+BnwfhHpARCRA0RkfuFKIhIFLGPMRuBTwAkFizwF7C8ir0svP0dEgsA9wNnpaYuAg9LL5pK7zDJSNr7CM08zSlufGVIURVEURVGUpqZn/qWceVPuPUNw5k2j9My/tJZmjTGPi8gc4CVjzFZgq4i8Grg/JY1jD7ASSBasegDw3yKSOWnyiYJ2YyJyFvBFEYmQul/oLcB1wPUi8iiQAM4zxkylt5XhcuAbIvIIsBc4t5bX6Adt+T1DiqIoiqIoitIyJKaWsmfb1diJ/bGCW+mZf2kN8gTFAzoYUhRFURRFURSlI2nLe4ZOPfVUA+hDH34/6oLWqz7q+KgLWrP6qOOjLmjN6qOOD6XFacvB0OjoaKN3QVEqRutVaTW0ZpVWQ2tWURQ32nIwpCiKoiiKoiiKUg4dDCmKoiiKoiiK0pHoYEhRFEVRFEVRlI5EB0NKx2Abm9GJUYb3DDM6MYpt7EbvUkugvzdFURSlXejkY5qIDIrIrVWs92MRmVdmmX8XkbdUv3eNQ790VekIbGMztGOI1XeuZnh8mMHuQdadvI6FvQuxRD8TcEN/b4qiKEq70OnHNGPMMHBG4XQRCRpjEiXWO62Ctj9d4+41jPb/yysKMDY5lg0/gOHxYVbfuZqxybEG71lzo783RVEUpV1o5mNaLBlbunXP1vte3P3ic1v3bL0vlowtraU9EfmsiFyU8/xyEfmIiDyWfn6eiNwmIncCvxSR2SLyXRF5QkR+ICK/E5El6WX/LCJRETlYRP4oIl8TkcdF5A4RiaSXuUFEzkj//DoRuU9E/iAi/ysic9Lr3iMiD6Ufb6jl9fmJDoaUjiCWjGXDL8Pw+DCxZKxBe9Qa6O9NURRFaRea9ZgWS8aW/mnnn24776fnLT3t+6cdfN5Pz1v6p51/uq3GAdF3gDNznp8J/K5gmROAM4wxbwYuBHYYY44C/g+w2KXdhcCXjTGvAXYCK3Jnikg4ve0PG2OOA94CTADbgLcaY04AzgLW1fDafEUHQ0pHEA6EGewezJs22D1IOBBu0B61Bvp7UxRFUdqFZj2mbZ/YfvWau9ZEc89YrblrTXT7xParq23TGPN7YH76PqHjgB3AiwWL/dwYkzkt9kbg2+l1HwMecWn6OWPMw+mfNwMHF8w/AthqjHkg3dYr6UvwQsDXRORR4HvAUdW+Nr/Re4aUjqCvq4/1b13Plt1biAQjTCQmWDBnAX1dfY3etaamr6uPdSevK7q+2u33ZhubsckxYskY4UCYvq6+trsOuxNeo6IoSjPiJX+dlvV6TJspEiaxv9MZq4RJ7F9j098jdY/QfqTO1hQyXkWbUzk/J4FIheutAV4GjiN1Mmayim3XBR0MKR2BbWwmE5Nc+dsrswF47UnXYhtbO7IlsMRiYe9Cbn7bzWUPPp1wY2onvEZFUZRmxEv+llq20mPaTBKU4NbB7sGDcwdEg92DBCW4tcamvwN8DYgCbwZmlVj2XlKX0t0lIkcBx1S5zaeA/UXkdcaYB0RkDqnL5OYCW4wxtoicCwSqbN939OittC25+syRvSNcfNfFeTdNXnzXxYxOjDZ4L5sfSyyikSiDPYNEI1HXg4bXG1NbUW/azDffKoqitDOu+TsxVnQsKZXVlR7TZpL+SP+la09aO5q5hG+we5C1J60d7Y/0X1pLu8aYx4E5wEvGmHIDq+uAARF5ArgSeBzYVcU2Y6TuCfqiiPwB+DnQlW7/3PS0I6nurFRd0DNDSltS+KnQD//hh443TcbteIP2sP3wcmNqq55hadabbxVFUdodt/zdm9jLB+74QN6xZE54TktldTgQvv/weYe//YZTb7g6YRL7ByW4tT/Sf2k4EL6/1raNMcfk/Pxn4Oj0zzcAN+QsOgmsNMZMishhwC+A59PLHpxeZjSzfnr6f+X8fF7Ozw8Ary/YlSHg2JznH6/qBdWB5u11KEoNFH4qlDRJx5smQ1aoEbvXlni5MbVVz7A06823iqIo7Y5b/j7/yvNFxxJLrJbL6nAgfP/+Pfu/4cA5Bx6yf8/+b/BjIOSR2cBv0mdufgBcmD7L0/boYEhpSwo/QfrmY9/kmmXXUHAKmmgk2qhdbDsyN6bm/o7dbkxt1TMsXl6joiiK4h9O+XvtSdey/g/r85YbHh/GwtKs9ogxZrcxZokx5jhjzLHGmJ80ep9mCr1MTmlLMp8gZTrcP3jmB8wNz+WGU28gYRKErBDRSJSgpW8Bv/AiWyj8+0Dzf2oH3l6joiiK4h9O+WuJVXTv72D3IJalWa1UTkOrQkS+ISLbMt+G6zB/mYjsEpGH049Pz/Q+Kq2J0ydIpx9+Ovt278uBcw5kv+79igZCrXhDf7NR6Y2peoZFURRFqZV9wvu4HkuaUZSgNCeN/lj8BuBLwI0llrnHGHP6zOyO0i54/QS/VW/ob1Va9QyL1omiKEpjcMvfw+Yd1nLHEqW5aGi1GGN+DTT3HdNKy+LlU6FWvaG/lWnFT+20ThRFURqDW/7unNrZcscSpblohYpZKiJ/EJGfiMhr3BYSkfNF5EEReXBkZGQm90+ZQep1KdtM39DfafXaLpcgtqr4wQ86rWaV1kdrExFHHgAAIABJREFUtvnxcmzo5PxV6kuzD4YeAl5ljDkO+CLwP24LGmO+mrZgLBkYGJixHVRmjswp8rNvP5u/2/h3nH372QztGPKlYz3TyuROqtd6/t1mmk5Wa3dSzSrtgdZsc+P12NDJ+VstIpJM33P/mIh8T0Rmp6cbEbk6Z7mPiMjlM7xvgyJya5Xr3i0iS/zal6YeDBljXjHG7En//GMgJCLqQu5Q6nmJkt7QXz/a6dIyrRNFURR/8HpsaPf8tWOxpfHh4ftiL7zwXHx4+D47FlvqQ7MTxpjjjTFHAzFgVXr6FPDOmehTi4ijn8AYM2yMOaPe20/vQ6DU/EYLFEoiIvsBLxtjjIj8FanB2/YG75bSIOp5irxVb+hvBdrp0gatE0VRFH/wemxo5/y1Y7GlU0NDt720enU0/tIwoQMGDz5g3brbZi1c+HYr7NuXr94DHJv+OQF8FVgDfLLUSiIyF3gEOMQYY4tIN/AkcChwEPBlYADYC3zAGPOkiNwATAKvBe4VkU3AF9JNGuBNQD/8/+ydeZwU1bXHv6d6mRWBYUAYkaAG9fHEGCFu5D01asQlGqMGFVTIooiJzyUxakzExBgToxKfIhojoKLB5RmJaIwLxkSTKBojatxXQIRhE5i1u877o7pneqma6Wa6Z3pmzvfzmc90Vd+6dWfq1t3OOb/LQ6q6R2Ky8gtgEuACv1HV/xWRQ4Bf4c1XngfOUtXmjPKdDFwCCLBEVX+QOL8FuBk4FDgb+GvQ39jT0tp3A38DdhORFSLyTRGZISLJmesJwCuJ3XCvB05SVe2p8ho9S7FN5L0xoL830NdcG6yeGIZhdJ1t6Rv6avsbr6+/JjkRAmhduYqV55xTG6+vv6aTS3MiYZ05AliecvpGYEpishOIqm4CXgIOTJw6GnhUVVvxJlTfVdXxwPeAOSmXjgQOUNXzE9+drap7Af8FNGbc5gxgNLCXqu4JLBSRcjzV6cmqOg5vQnRWxt9VhzeJ+hKwF/AFEflq4usq4B+JTWQDJ0LQ82pyJ6vqCFWNqOpIVf2tqs5V1bmJ729Q1f9M/CH7qeqzPVleo/tJDa5EYe5hc9NM5HMPmwtKTsGXfSWIv5Tx+x8HuTYMKhtUMs/D6oZhGEb30pHbWzHb5Hzy7q6+QWOxEcmJUJLWlavQWGxEF7OuEJGXgGXAh8Bv2+6p+ine1jbn5JDPImBy4vNJwCIRqQYOAO5N3ONmILW896pqPPH5GeBaETkHGKSqsYz8DwVuTp5X1fXAbsB7qvpmIs0CPItSKl8AnlLVtYlrF6akiQP35/C3lbabnNG/CdpT4O6j76Yp1kR5uJy1DWuZ8tiUTvd8sf1hik9H/+NM14ZBZYN4Z+M7JfE8rG4YhmF0P0Fub0DR2uR82vvu7BskHP44skPd6NQJUWSHOiQc/riLWTcmrDFBzMYTK5vXST6LgStFpAYYDzyJZ3nZ2EH+W5MfVPUqEVkCHInnNnc4nhtdMWlKmYx1iPX0hi+lsFK+vmk9N/7zRi7c50LmHT6PC/e5kBv/eSOuutRV1+Gqm3PwZV8K4i9VOvofZ7o2bGzemPfzCKqTXa2rVjcMwzBKh2K2yfnk3Z19Q6i29oIdrr++PrKDZyWL7FDHDtdfXx+qrb2g4DdLIWGBuQf4ZifptuDF7PwaL84nnrAsvSciJwKIx+f8rheRXVR1uar+IpHP7hlJHgPOTIotJCZdbwCjReSziTSnAn/OuO454EARqU3EHZ3sk6ZTzDJkZFEqK+Wu63LK2FO47JnL2spx+cTLcV1vsJtP8GVfCuIvVYr5PDraebyrFiarG4ZhGN1PULs+IDqgaG1yqY4bnGj0b2VjxhzzmTvuuEZjsRESDn8cqq29oIDiCR1xDfCdHNItAu4FDko5NwW4SUQuBSLA74B/+Vx7rogcjCeO8CrwCOkudbcCuwIvi0grnoDCDSIyHc8NLymgMDc1U1X9WEQuApbSLqDwYA5/SxpmGTKyKJWVche3bSKULMdlz1yGizcZyif4sq8F8ZcixXweQXWyvrG+y3XV6oZhGEb3E9SuO+IUrU3Op70PKkexFoWdaPRvkbq6A6KjRu0Uqas7oBATIVWt7uy8qn6iqpWqOquTvO5TVVHVP6ece09VJyVi+8eq6k8S56ep6n0p6b6rqnuo6p4JvYBmVX0/IfmNqsZU9fxEHp9T1RsS559Q1c+r6jhV/UZSSU5VD1LVZYnPdye+3yOpJNfR3+6HTYaMLAq9GrKtwYoxN+ZbjuT1+ew50Nf3JygFivk8WuIt1FbUMvvg2cw7fB6zD55NbUUtrW5rl+uq1Q3DMIzuJ2is4eDk1SbnM8aoKa9h7mFzmXPIHOYdPo85h8xh7mFzffN2cLh84uVp5bh84uU4NnTuc5ibnJFFcuUktZHa1lWZrgQrzjlkToflyGfPgb68P0GpkO//OBqKcul+l1IRrqAx1thh/SoPl3Pu3udy6TOXttWjKyZeQXmovMt11eqGYRhG9xM01nCc3NvkbXHrb4m3cMXfr0hL74fjONz12l1cuM+FDIwOZFPLJu567S5+fMCPC/MPKEFE5IfAiRmn71XVn/VEeboL6Yvb9kyYMEGXLVvW08XotXTWuLjqsr5pfWAjlfq9Iw6nP3J6VmO38KiF1Fakb3xc31jPlCVT2tLuWbsnl+xzCeub17cNmEcOGMlntvtMTw1UpRiZlkp97ey5FpL6xnoWLF/AV3f9KiEJEdc4v3/z95w+7vSsegFQ31DPlIenZNWjO4+8M83VwpTgsujTddbok1id7eXk2pcUIj45c9wA7WOMmvKarHKsb1ofmD6z78mjfEWps0b3YZYhI4uOVspzmSilfn/7pNu7FKzYGG/MaQXH6BrdLZohKkzaeRIzH5/Zdr9rD7oWUf8+pcX1d6dodVvNqmMYhlEi5NuX5OMh4EeQq53rdl2cwbwG+g/2RA1fgnZ57kxcIfP79c3rtzmo/hvjvsEP//rDHhdy6A90t2hGi9vC+U+dn3a/8586nxbXP9ano6DXvrojuWEYRm8jX+nqGY/NYOYTM5n+6HRmPjGTGY/NKIgAjov/1hv5ijNY/9I/MMuQkRediSu0xFvYd/i+nL7H6YQkBMC1B13bNvBNrs444rBqy6q0lZZkIHuyAaspqzHJ426iu+Wlg8QxYlmbUntk1o3UgNqYG6O+sZ7WeCuRUITailrCjjVthmEY3U13S1cnBRFWbF6R5k7vqtuhOINfX+JHd7qPGz2HjRiMvOhMXKEyXMnk3SenuT9dd9B13HnknbS6rUSdKJtbN3PyQyf7mtBTTdLJFZxCCDkYHVNI0YxciIQivveLOJEOy5jpThF347y18S3OW3pee307+Dp2HbyrTYgMwzC6mXz6kkL1O36CCAPLBvrmHQ6Fc3bNK5U9F43iY0/TyIvOZIibYk1Z7k/nPXUecTdOXXUdCMx4bEagCT3VJD2scphJHncT3S0vXVtRy3UHX5d2v+sOvs5XPAGC3SnqG+vbJkKQqG9Lz6O+sb4o5TYMwzCC6e4tL4Lc8lzX9c3bdd2cXfNKZc/FriAicRF5SUReEZF7RaSyo/N55FsnIvd1njLruodFZFAnaX4iIofmm3dXsKXTfkpnpt9M16MQIZriTURCEXYeuHNgQGHQvi+t2grkZxa34MXuozPRjFzdBIJc1vzy2HXwriw4YgGtbisRp+O0QfUm0N0uUQ6rN4ZhGF0jnz4g3y0vdhm0S1Y/kE9bndyDLlX++rblt9HitrTnndIfrWlYEzgGyey/glztiuU+Hm9192/Y3HKNG3dHOCHn48oB0QtCEaerG682qupeACKyEJgBXNvB+ZxQ1VXACZnnRSSsGuDv7l13ZA55d7t2eY9OhkTkNuBoYE1yF9qM7wX4NXAk0ABMU9UXu7eUfY/OTL8xN8abG95Mcz26YuIVzH5xNvWN9R26IXXm/pSvWTxpKTKKj9//Oh83Ab96c93B1zFm0Bje3fSubx7Dq4bndL+hlUP9XR6csO95VW2TTzXXBsMwjG1jW1zFcu23XXV5Z+M7XXJDC9qDrjJc6Zt3UF9SGa7M6r9u/fKt3eY+Hm9191/38dbFf7x5ee3mdU0MGFI+etKZ4xYPGVF1TAEmREn+AuyZx3lE5CrgI1W9MXE8C9gC3Ac8pKp7iMg04GtANRASkSOA+cAewBtAHXC2qi4TkfeBCYm0jwB/BQ4AVgLHqmqjiMxP5H2fiHwBbx5QBTQDhwBDgDsS5wC+o6rPbtu/xKOnRwbzgUkdfH8EMCbxcwZwUzeUqc/Tmek30/WotqKWpngTP534Uy7c50LmvjSXtQ1rfXd7jjpRrph4RZpp+oqJVxCSEKu2rMKR/HaWNnqWfNwEOnJZ88vj0+ZPWb11NR99+hGrt64m5sbydnnwc7ebffBsrn7+6l7t2mAYhlEKFNNVbH3Teh56+yHmHDqHP3z1D8w5dA4Pvf1QXnm7rts2EUqW79JnLqUp1pRXX9IUa8rqv65+/mpmHzy7W8YrDZtbrklOhAA2r2vijzcvr23Y3HJNIfIXkTDemHp5LudTWAR8PeX464lzmewNnKCqBwIzgQ2qOhb4ETA+IO8xwI2q+p/ARuD4jLJFE/f6H1X9HHAo0AisAQ5T1b2ByUCX91zp1DIkIhOA/8Kb2TUCrwCPqeqGrt5cVZ8WkdEdJDkWuF29nWH/LiKDRGSEqn7c1Xv3ZzpzVWuNt7u67Vm7J9/d+7tc9sxlbasll0+8HFddJv3fpKyVnKZ4E7NfnJ1msp794my+N+F7nPbH06irqmPuYXNZeORCWlxzYSp18nFrTK03qWn9XNn2Hb5v22Qp1Yo0qGyQ//3clkDXi0x3OweHpSuW5lRmwzAMI5hiKo3mu9+cb/k62IMun75k5eaVWemXrljKxftd3C2u+m7cHZGcCCXZvK4JN64juph1hYi8lPj8F+C3nZxPQ1X/KSLDRKQOGIo3yfnIZ+z+mKomZ7FfxLPmoKqviMjLAWV7T1WTZXgByMxzN+BjVX0+kdenACJSBdwgInsBcWDXgPxzJvCJish0EXkRuBiowDN1rcH7Ix8XkQUiMqqrBeiEHYCPUo5XJM4ZXaCjPVug3dUNvL1+khMh8BqTy565DEXbjlNXiaKhKPWN9Zy79FymPzqdc5eeS31jPeub17eln/HYDBBMt78X0FldSSW13qSmTbqypTJtj2m+ViRBfPNwxAnc7yHshBleNZwdB+zI8KrhOE5++0gYhmEY/uTTB+RLvvvN+RG0b5Bfv9PR3nRB/Zcg3bLPkBNyPh4wpDzt3IAh5Tgh6erif6Oq7pX4+a6qtnRy3o978eKDJuNvFQLYug1la075HCf30J3zgE+Az+G53HW5Mnb0VCuBiap6vKpeqaq3quoNqnqOqo4HrsMzcZUEInKGiCwTkWVr167t6eKUNH4KLnMPmwsKq7asIiQh5hw6h7qqOgZGB/qurnza8mnacXKVyC/vyydezm3Lb2tLX1tR6602+bjZ9Rd6S33NR+2nI4W4zDzCTti3XgH87Is/S0v7sy/+DCcPj97kvhNzDpnDvMPnMeeQOcw9bK65YnaR3lJnDSOJ1dmuU0yl0Xz3m/PDweHqA69Oa++vPvBqok40sNyuutQ31qeNQfJVOC00lQOiF0w6c1x9ckI0YEg5k84cV185IHpBtxSgYxYBJ+FNiO7NIf0zJFzrRGQsMG4b7/sGMCIRN4SIDEi49Q3Esxi5wKlAaBvzbyNwFpYMlurg+5c6+r5ArAR2TDkemTjnV55bgFsAJkyYoMUvWu8lU+2lPFzO2oa1THksPeA8uTeQXwDhx1s/TjtOrhL57RV05d+v5OV6z0q6Z+2enLv3uUz/4/R+HdzeW+prPspAYSccqBCXmUcsHvOtVyEJURGqSNsDoiJUQTiUn9aL374TRtfoLXXWMJJYne06xVR13Zb95jIJh8LE3Fhae//z//o54ohvuYFAQYig/qs7CEWcvw0ZUXXMcRfsfY0b1xFOSAqlJtdlVPVVERkArMwxTGUOsEBEXgNeB14FNm3DfVtEZDLwvyJSgReqc2gi//tF5DTgj2ybVSoN8cJxOkggshPwXTxfvrZaoarHdPXmifxHk1Ck8PnuKOA7eGpy+wLXq+o+neU5YcIEXbZsWSGK1y+ob6xvU95KUldVx8KjFlJTXpPVcFx38HXMfWkuS1cs7XQyk6lEM+eQOW2NVua9eoFqXO6OzHnQG+trV3flDlKeG1oxlKkPT82uH0cupKaiJqd7dlSfe0EdKzRWZ43ehtXZfkJQP7Dr4F1xxMmtvW+oZ8rDPu39kQuprcxu74vUPxSlzvZWRCQERFS1SUR2AR4HduvEFa9HyWXK+3u8wKo/AAX1ZxKRu4GDgFoRWQFcBkQAVHUu8DDeROhtPGnt6YW8v+HRUYCkqy5hJ5y2Uj8gMoAf7f8jLnIvynvPge7W7TcKTyF25Q6yIgXuAeG25HzPYgb8GoZhGIUhqB9wxMm9vQ8QUAiKO7L+oVuoBJaKSARvojizlCdCkNtkqElVi+Jjoqond/K9AmcX495GOx3t/VPfWN+m9JL63YIjFlBXXeeXXRapew7UN9Z3m26/URyCpFbzXVlLCh+kElQXHXFyvme+e1kZhmEYPYNfPxC0HUMh2vuk4IJfH9OfEZEhwBM+Xx2iquvyyUtVN+MJG/Qacnn6vxaRy0RkfxHZO/lT9JIZ3UZHAZJ+csm1FbWehSdAAMEvODGXexm9g0KtrMXcWNY+Q0H1w8HJ+Z5WxwzDMHovnXmrpI4vBpUNyqu9d3C6LNLTF1HVdSnqcqk/eU2Eeiu5WIbG4ak1fIl2NzlNHBt9gI4CJDMDHDsTQOjMhaqYwZhG91AIy0tHvuJ+9WN90/qc72l1zDAMo/cS1MeUh8t9xxe7DNol5/Y+HAoXRKTH6FvkIqDwNjC21P39UumvgZKt8VbqG+uJuTHKQmWICK1uq9c4RAfhNK6DWAuEo1A5FJzgwWFqgHzcjXP181ezdMXSTgUQcglO7GrwfQ9igb14z++DTz9gxeYVbZ3JyAEj+cx2n2lbtWuNtxIJBavxrN66miv/fiXHjjm2bXPeB996kEv2u4RhlcOy6gcEKwD1krrTU1idNXobVmd7CYF9uetCw9qcxht+eQC+fcyAyAB/sYQ8XLQ7ElzIVaTHBxNQ6OXkMhV+BRiEt+GqUaK0xlt5a+NbnLf0PGorajl373O59JlL2weOB13HmMd/hvP6QzBoFJx0Nwwb69tA+Vl3Zh88m4v3u5hY3H9fgKS7UmcuVIUIvjd6FjcepynWlCZlOvvg2cTirby96R1fa0/mhEhVOWXsKW0b+ib3o1I0cOUvGoqmreZZDJBhGEbPENiXD9wFZ+3r8LuTYeOHHY43gvLYeeDOvn1MWaisyy7aHQku2Nik/5LLEx4EvC4ij4rI4uRPsQtm5Ed9Y33bIPQb477RNhGCRPDhU+exfu8pXuKNH3oNVYP/JnR+AfLnLj2XiBMJ3KU52Vh0tlt1UPD9+qb1hflHGEWnvqmec5eem1U/1jWtb6uDyfPnLT2P+sZ633ySE6Fk2sueuQwU3/pR31jPjMdmMPOJmUx/dDozn5jJjMdmWL0xDMPoAQL78sa17RMh6HC8EZRHfaN/HyNIh+OPXEgKKGTmIYiNTfoxudSgy4DjgCuBa1J+jB4mNZAwdSfngdGBvisfTUN3Y9W3HqX+lLtxq4d5JmwfWuIt7Dd8Xx6d9AeePPxhHp30B/Ybvq+3iSoOl0+8PC348PKJl7cFH3YWvG6ylsWhI9GKXInFWtmw/hPWf7KKDes/IRZr9U3XGrRreMD5Vjc7H0V908Y1HpiH1RvDMIzSILAvd2PtE6EkGz/0xhuuC1s+gY0fwZZPAvOIuTHfMQjQZfGDoDEMQr/tY0TkYREZVMD8bhWRsXleMyOxiWpHaSaISFHUrXNxk/sQ+FhVmxKFqQC2L0ZhjNzJNC8/eOyDbQGHm1o2+QYfvrd1FTOfmOlNUI74GWMiFb5NSFW4kjN3OJUlP/0Jn65dw3ZDh3HmBRdTFa6kVWPc9dpdXLjPhW2xHne9dhc/PuDHADgKY9wQCz9/IS1lVUSbt1LjhnAUEJM9LgaFcD2MxVpZ+9H7PHTNz9ue+dEXXMzQHUcTDqfvBh5xwr7PMBxw3m838ajjXw/CEpyH1RvDMIzSIKgNjzoRzzUudUI0aBSEorDmtTT3uej0Jf5CCaEy3zFImUS7LH7gOI7vGOaS/S7p8T4m1tq6f8OmDde4sfgIJxz6uHLg4AvCkcjfinU/ERE87YAjC5mvqn4r4H4hVY0HXDM3h3yXAUUJ/MtlpHQv6ZutxhPnjG4mdfV/TcMabvznjW0v7vxX5nPtQddSV1XHbctv44qJV6StfFwx8Qrm/sura6u2ruLG1+9ijcZ8LQna0MKSxKAY4NO1a1hyzc/RhhZqyms4+/Nn88vnfsn0R6fzy+d+ydmfP7tdxrJhLc6dx1F7x9eou/Vwau/4Gs6dx7WZyE32uPAUwvVw86fr2yZC4D3zh675OZs/zc6jtryW2QfPTnuGsw+ezZDyGt/zfoGtNQrXT0xf4bt+4s+odaK+9aO2otbqjWEYRongOI7vOMMJRb0YoUGjvITJmCEnlOU+V/PIxVx/8K+z2vVos/iOQaQ5xs0v39y2oWqL28LNL9+M6+buCRE0hunpPibW2rr/uo8+WLxo1kX7//Z/vj160ayL9l/30QeLY62t+3clXxE5X0ReSfycKyKjReQNEbkdTxNgRxF5X0RqE+l/lPj+ryJyt4h8LyDf3UXkuZTj0SKyPPH5KRGZkPi8RUSuEZF/AfuLyDdF5E0ReU5EfiMiNyTSzUreK3H9LxJp3hSR/0qcP0hEHkp8rhaReSKyXEReFpHjE+dvEpFlIvKqiFye6/8pl+l0OFVJTlVbRMSWY7sZv9X/yydezrqmdbxc/zIPvPMAAPMmzSPuxikLlXHnkXe2uSh9/8/f5+X6lwFPHvuUsadw+h9P95fHjsXbGqEkn65dg8bincsWx1qCTeR0bjky8qcQrofaGvzMM3FCIcrD5Wmrc+Xhchwn4LyfdSreStSJpAsiOBGcWLN//cDksg3DMEqFplgTs1+cnWZhmf3ibK4+8GpPLOFbj6eryX26Mmts4FYMIhxK7wfCTph4SyywP/IT3snHLbyjMUxP9jENmzZcs/jaK2tTJ4CLr72ydvKsq67ZrnbYAduSp4iMB6YD++KNsP4B/BkYA5yuqn9PpEum/wJwPPA5IAK8CLzgl7eqvi4iURHZSVXfAyYDi3ySVgH/UNULRKQOuBPYG9gMPAn8K6D4YVXdR0SOxAvXOTTj+x8Bm1R1XKLsgxPnf6iq60UkBDwhInuq6suB/6TkzTpLAKwVkWNUdXHihscC/hHRRtHwW/2/7JnLuHCfCzl36bkA/GP1PzgndA611ekr8fWN9WlB7N8Y942s4PXU3Z0l5LDd0GFpjdF2Q4dByGsUHHGCZSzDUX8TeTgxf05ajjK//9bjUG3el9tCIVwPJRzyfeYSCmWlXd+0nhmPzci634IjFvie95M9Xe/AjL9cmJ328HnUBtQPp3r7nOVTDcMwjOIRVdqEDpLUVdURVTzVuMz+3GdsUP/f5zPz8ZlZ/cDvDr49sD+67Ols4Z0FRyzIq+xBY5gOxzZFxo3FR/hNAN14fEQXsv0i8ICqbgUQkf8D/gv4IDkRymAi8GAiLKZJRP7QSf734E2Crkr8nuyTJg7cn/i8D/BnVV2fKM+9wK4Bef9f4vcLwGif7w8FTkoeqOqGxMevi8gZePObEcBYoNPJUC5T3hnAJSLyoYh8CPwAOCOH64wCErT6X1PmmXCzTLopgYo1Lsw9bC5zDpnDvMPn8dlBn8164VdtXUVTrIlVW1ah5WGOuuBibwKE1wgddcFFaHkOppvKof4mcgl5QZOxFqgeln5NiuXIyJ9CuB4OKA9zdMYzP/qCixlQnr1eElQXW91Wais8F7p5h89rc5Hzs1C1oP5pNZ5z8C15uEYYhmEY24hP21vjuly/32Xp/c5+l1ET1C77jA1aE3vRZfYDsXLh6AsuyeiPLkHLw759z7YIBpUaTjj0cfLvTbLd0GE4odDHRbjd1gLlswhv8rEroKr6lk+apqA4oU5oTvyOk5vhBhHZCfgecIiq7gksAcpzubbTG6jqO8B+IlKdON6SS8ZGYQla/R9RPYJHj380e8Oz1EDF3Y+m5bAfpmn2XzHxCma/OLvNda6uqo73Nr3XJrAw50s38pUfzyLkOrRKjN99cD+nDT2l84I6TrqJPBSF5s3wm4Pb9xw4dg48MQtWJOLgUi1HRt4Uwrwf1hhDP3mSE2b9DI0rEhIGvL2Y8NCvZqUN3B3cKc/a3+qKiVdQHspui8rDQWnL/C2LkYqs4NuO9soyDMMwCkDmeCLR9jpVtYz5640s3Pt/aKmsIdqwnpq/3ohzxC/888kcG4SjlDuObz+ACMNGjebEn1yFxuJIOMSAgTVsaNnY4yIHxaJy4OALjjn/ksVJV7nthg7jmPMvqa8cOPiCLmT7F2C+iFyF5yZ3HHAqwQaNZ4CbReTnePODo4FbgjJX1XdEJI7nsubnIpfJ88DshEvbZjyXvOU5/i2ZPAacDZwLbW5y2+FN9DaJyPbAEcBTuWQWOIoQkaki7aMpVd2SOhESkV1E5Ivb8hcY+RO0+j+schh11XXUVtS2D34b0nX+1+89hXMy9n+59JlLmfG5GW15ZQoszHzybN5q+YAvPXoE0/9yJkftfAQ1FUPzL7jbCo/9OH3PgQdnwoE/8I4HjYLJC9tXnmy1f5tImvez6kKuVA4lPHoig+/8MjU3/QeD7/wy4dETvdW8DILqIhrP2t/q0mcuxY1nW3VcNyCtCEx9AKbcC9OWeL+nPgBuPHjvCrMYGYa+WfWFAAAgAElEQVRh5Eeu7WZDwL5BgPOlH1ErYepiMWoljPOlH0H18OC8k+5zg3aE6u2JuzHffiDuxgiFwgyqGcbgYSMYVDOMUCjcpwWYwpHI34bs+JljJs+66m/fvP4370+eddXfhuz4mWO6oianqi8C84Hn8OKFbgU2dJD+eWAxnlvZI3gTlU2d3GYRMBXPZa6z8qzE26bnObyJ1/s55B/EFcDghDDEv4CDVfVfwD+B14G7EvfIiY4sQ0OAf4rIC3g+e2vxzE2fBQ7Eixu6aJv+hAQiMgn4NRACblXVqzK+nwZcDaxMnLpBVW/tyj17K3mt/meIGLRU1vialneq3oFHD/kNDBjO9//6wzYrUdv3232GR49dTNSNUyPR3AbYfqtIx9wAWz9ptwRt/BBqd4VzXwE3Bo/+EN5YYqv9PYnPqh2VQ32fQ1BdXL15ZcC+E61ZdaJl+kPBaWNNsOSCdAtQtCrAfa7ZLEaGYRj5EGDt8W03Y83+ba+qt9iZ1lbf5X2fY95Be9a1ujHfYve0yEGxCUcif9tWsYQgVPVa4NqM03tkpBmdcvgrVZ0lIpXA0wQIKKRc+yvgVxnnDkr5XJ1xyV2qeouIhIEHgN8n0s0KuL6eRMyQqj5FwtKTMM6c7lOeaR2VN4jAGqSqv8ZTfLgbGAockjheCZyqqscH+AfmRELp4UY8M9ZY4OSATZoWqepeiZ9+ORFKkvPqfzJQMUG0Yb3vjsvla16n7tbDiX7yWprAQtv3q1+hbvZe1F4/HmfBUb47SGfht4q0+DswsT3Iss3tKRyF24/xJkLJtAE7VRvdQMaqXUeTCb+6GJWQbz2LSrakarT+bf+0iP8qpBtLq9NA4jggvdUhwzAMf4KsPb7tpvi3vW4MfndKRh6nwJbVOeed3LMuFW9fueB1+i57QRidcYuIvISnJHd/wrpUSGYl8n8FeI/EZKin6bAWqWpcVR9T1VmqeqaqnquqN6vqhx1dlyP7AG+r6rsJ6e7fAccWIF8jI1Cx5sWFXH/Qdemm5QN+Sk2kGqYtoSZSzfUH/DQ7EPLpa9rzTBU5yDSBx2Ptx62N/qtIVQl3q+QqUeXQTmW4jd5FjYS5/sBr0uvRgddQg5P1nGuWXsn1GXsSXf+l66lxCViFjHsWxlRhjmNuABGrQ4ZhGPnQUd+b2b+LAyfMT3ddPmG+NxnyyyPemnObXFtey3UHp49Nrjv4OmrLTTW0p1DVUxLGh91V9ecAInKjiLyU8TN9G/P/Xkr+56iqFvYv2DZy37a38OwAfJRyvAJPCz2T40Xkv4E3gfNU9SOfNEYqGS5PTjjKmIoh7aZlhJpNH+M8eDZs/BBn0CjGnLiAhZPm04J63z/0PZwVKRv9JkUOMs3rux0FB14I95zqHU+51z8AfuBIzy0u1f2qMxluo1fhqDLmxUUs/NINtDhhom6Mmudvx9nvzKzn7FRt77+fUChg93InDP+4GQ6/EioGQ+MG7/jIX1odMgzDyIegvjcUzXZxm/4IxFvS3eGOuwXC5QF5BLThPm1yOBxh14FjWDBpPq1ujIgTpra8lnA4UsQ/3sgXVT27p8tQbKSnJmUicgIwSVW/lTg+FdhXVb+TkmYIsEVVm0XkTGCyqn4pIL8zSChkjBo1avwHH3xQ9L8BvM1Q1zetL4z/qut6puSgmI3M7yuGQOO6TmM8gIT1ZrW3auOEYf6R2Y3V9D+Cuu0KcHce1974TV7oWXc0DvOOaL928p3w6CXtxyMnwCGzPJGEznyR8/FbLg0Kti1swetrZ3Unlyy6Wpe31kP9W/DAGemdZu1nvVXGhnqIVEJrAwz5LNx+bHYd/ObjsHVtdp0YujtseB82vNuex+CdYfBoWPu6f/pc342+TenWWcPwx+pssQnqewcMb1d+TTJtCfz+LP/xwpbVcO+09jxOnA+Dd4KG9dltdc3OuELhxkulhW0Z38vpScvQSmDHlOORtAslAKCq61IObwV+GZSZqt5CQgJwwoQJ3TLDc9XlrQ1vtW2GmnT1GTN4TP4veGcTA7/vv34H/PmXnYsPxGPwySvt1ptvPOpvxt70Edx2eHte317qub2lihxkXlsxOP14xTJPNnvaw95xRwPRPIL2+xoFra8FmFQWpC7HW72J9FHXtHeCoaj3bFsb01cWT/19gItFs3+dAH9hBfGpQxVD/CdIpTvJ7hX0RBtrGF3B6mwAQX3vpyuz22Xwb6vdGESq0tv7SJW3p6BPW+2qy1sb3ynMeMkwCkynNVBEykTkFBG5RER+nPwpwL2fB8aIyE4iEsXbSXZxxr1Td949Bvh3Ae5bMNY3rW97scFTQTnnyXNY37Q+/8w6C2j0+/6eU2Gvk/3Tp7JldftECLyVd7+AyK1r0/PSeLbIQea1jRuy89qyJmGG7zwQP5+gfSOAvIJh/SlIXdY43DcNFp4I84/yft83zbM2/n5GevnceLAggl+d6OhvzEzfuM5EFQzDMDrCr50VH7GE1gb/tlocuOvE9Pb+rhOh+VPf9nd949rCjZcMo8DkYhl6EE8H/AXad4TtMqoaE5HvAI/iSWvfpqqvishPgGWquhg4R0SOAWLAemBaoe5fCFriLf7ywPFtCN72C2isHpY4/1H7cWqajR96lpnUY7/A8cyAxmdme8Hni7+TLn/95E+y7+3GvDiNZ2Z7Vp/Ma1+6G065FzZ92L46VLs7KF65+5G1p8cICoZNBsLmYHXrsC7n6oKnGlCOePb51q3+dVDE/375iG2YMIdhGEbH+LWzEvLEERrXtffn24303OQXTUmX0A4SrwkQVmgJkNHepvGSYRSYXCZDI1V1UjFurqoPAw9nnPtxyueLgYuLce9CEA1FC7cbcmZAYzL2JhnbM2gUHDvHc0FLChsMGuVZZpIEBY5nBjSuWOYFnydd2UTg4Qvb8/W7d3KylLx2+iPe4DdSAZs/TjeJ5+q+ZxQGv2DY3Y7yrHhpHVjwcwisy45PQG1QPoFBueHs81vXwrL5PoIIVwf7sucqlGDCHIZhGMEEuVZvV5ctlnDCPKgcku4O50S8/j9I7MbnfDQho12Q8ZJhFJhOBRRE5Bbgf1V1efcUqetMmDBBly1b1nnCLlLUmKEp97Y3SEkGjfIapIUndi1mKHnt9nt4A1XXhfXvtgc8Vg2Dx2e1u8Yl733qA166RDAkjuNZHm49NLuch18Ji6a2H3/rcc8U37spSpBkl+urX8d22mLPvTHzuQQ8h8C6XDYEJzOgNiifoA7WT/xgyK7eJDpVbOHYOd5mvL/1qU9Bwgp9Q5ijmJRmnTWMYKzOFpugfnv6I97C6F4nty9SvXQ3TJjmjTtS0357KWxakT2uGLY71L+d1f66Q3fnrU19NmbIBBR6OYGWIRFZjufsFAami8i7eG5yAqiq7tk9RSxdCrobcmZAo/q4Fm380BssJiWqK4bAV66DI37RsftSKOxNfKY/kghyj0D1cO98ksyAx2NugK2ftFuLNn4Im1d7aU66O+W6AJekXNz3jMLgFwybp6tYYF3e5BNQG5RPUFCuup5bXGr9mrwQXroz3TL0xCz42q0BwgotuYtt9GNhDsMwjE4J6h8A9j0z2325fLvstM2bYdh/eB4mbsyzCFUPg3CZb/vrOAUcLxlGgenITe7obitFLya5G3JhMnPaV9u3fOJvgo5UpK/IZ6zOq6s0bG7Bjbk4YYfKAVHEEW/iM3Ck/339gtMXfyfbutO4oT0YPWkZCHJJysV9zygcqXUHgutPB8/Bty7n63KWWQ6ATau8epRavxZNabdyJtDdvkIDtbinv4jTsIrKZy9HVj7ffj+/vAP/mDzSGoZh9CeC2nXV9okQtI8Fptyffn1yP6FwmSfAkElA+5vPeClwLGMYRSBwMqSqHwCIyB2qemrqdyJyB3Cq74VGYagc6llgUk3NUx9IFyYor/GsNwlrj1YNZ93qRh6+aTmb1zUxYEg5R874T4bUxJCKwcEr40GrRFUJSeNMgYVUy4BfOZPue8lrT7q7XR7Z6B78nsu2PIegfCqG5CzOELgj+ZDPeu6gkUrUibCO3Xn4mpS6e/qtDHnhUuTgi6z+GIZhFIrKod54InMvoCCPlNS4z2QfXz28IPvb+aGusm7VlvSxzFnjGFJXbRMioyjkIqDwn6kHIhICxhenOEYbma4+kQrPTS25Eer+58C449P8dRum/pmHb3qdzeuaANi8romH577K8aeGqRpUGRwzEbRKNHAk/M+/oP7NdvGE5HdJy4CfS1Ku7ntG8SiUq1jQ881nH59QQP2SUJvrXMNJj/HwHa+m190Fqzj+e3OpGlhp9ccwDKOQ+O3bFq0J8AQoz3azF6dosZkNm1vaJkKQ6A9uWs7xF46namBZl/I2DD86ihm6GLgEqBCRT5OngRYSm5gZRSbTbS7Vle3zU+CJn6TFXLgNW9sajySb1zXhRnaA350QLGIQtPo/oM77vnmLt3cQ+FsY/Ezi5qLU8xTKVczPBc9vH59vPe7Vi8yVwnCZJ47w4Mx0sYTNq9rycCMD2bwubc9lr+66YZsIGYZh5EKulpqgfdu+8Sc4cQE01LdbjCprvYnPdnXpeXTUD3Sx33Fjrv9YJmb75hrFoSM3uZ8DPxeRnyckro2eJNOVLVKRFejonPIkA4aUpzUiA4aU4zSt6VjEoDMrggWjG6l0tK+R30ph5RBPHCFTLOHQy9sud5rW+NfdsLlEGIZhdEo+KppBbbjGobUx3WL01bmeQEImRdzPzQk71h8Y3UouI9p7RWTvjJ9dRCQXFzujUGTtDJ0d6Fj5xHkcOeM/GTCkHPAajyPP2I3KZy/vXMTAbzfqXL4z+h9+u5QPGuV1mH4rhRr3LIuLpno7lS+a6h23NrRdXvns5Rw5bcf0unvWOCoHmPCGYRhGpwRZexrWZqdNusankhRQ+P2M9Dx+P8M7n2seBRBLqhwQ5cizxll/YHQbuUxo5gB7Ay/jucmNA14BBorIWar6pyKWr38Tj8GW1Z6fbrjcW6FJNlRNn2atysgbf2DIpJ9z/P/sius6OJs/oDLyEbL1k2zxhYoh3i7TZu3pG/i5R0B+wa1BLhaZ50NRT1AjU37VDQi+Vc12w5y80KvXCf902foJQyrWcvwFe+JqGCcsph5kGIaRK/lYaoJc44MEFDSeex4FELsRRxhSV83xF47Hjan1B0bRyWUytAr4pqq+CiAiY4GfABcC/wfYZKgY+G2UeuIC+Mr1XgBjQFC61P+bquSmrMfOgerPepujpYov7HYUHHhhet79d1PK3k+Qe0S4vP2Zd/aMO9owNVMsYfJCeOvxdLe3f9wMR17tH3wbima7WkoIFp+Tlof85VdUfeU6izczDMPIlyChmlCANSVc7m1vkIwNCpeDE/HPw4lkX1/k/dzEERNLMLqNXGrtrsmJEICqvgbsrqrvFq9YfRjX9QIPN37k/XZd/3RbVrdPVsD7fe/p4LZ6rkZP/MSTt0yaqZOTnz//oj39gzO9wEeNp5vP9zo5O+8gc7pR+gS5R2x4N/dnHJTHltXZ5xdNgX2+5QkjgPf7sJ+0CyVk1kknlO1qWVEDB18Cj17i1edHL/GOTULbMAwjf5yQf/sbimSPORrWegtlC0/02t+FJ3rH4gS34X7jFnOhN/oIuViGXhWRm4DfJY4nA6+JSBnQWrSS9UXyCXAM2ptl8GiYtsRbyamogW8+DvGEGfy+ae3y18n0ye9S86oYXLTAR6MHCHKPiFRmnwt6xkF5BNVDN5YeZDv5TohW+QslnDA/+35FXlU0DMPoV7Q2Zre/L/8O9jnTW8BKHXOUbxcwBmjyb8O/9hu4/VjzJDH6LLlMhqYBM4FzE8fPAN/DmwgdXJxi9VGCVt/9pChDAebqtW94AejJ42897q3KbFrRLn+dml4kO6/GDQF7CVhwYq8kaJ+oFIGCtnNBzzgpipDlHhH2P7/+nQxr0VSY9nC7UEIu9yyU9LdhGEZ/JxzNbn+n3Ns+EYL2Mce0h4Pbe782PB4rioS2YZQKnU7rVbVRVa9R1eMSP79S1QZVdVV1S1duLiKTROQNEXlbRC7y+b5MRBYlvv+HiIzuyv16nHwCHKuHe6vtqebqr86FZ2b7XyshL4g9Nf0xN3jnk4GOye9eujvbxa5AgY9GD5D5fJPPc/DO/s/Yz1UzqP6Ey7LznnxnuztmkmSQrV85rF4ZhmEUF79+oGYXqB7mtdnTlni/q4d5i19B7X3m2ODrd8Czv06/l3mSGH2MTi1DIjIRmAV8JjW9qu7clRuLSAi4ETgMWAE8LyKLEzFJSb4JbFDVz4rIScAv8Nz0eidBK/h+K+fiQKQqPcAxUpGeJvVax/GC2DOD2r9ynb9LUsUQc1HqKwS5nIH/OT9XzQHDg+tPZt6q/lZIJ2yub4ZhGD2BXz8gIThkVvaG10gH7f1/epYjN+a16dEqeO/p9HuZJ4nRx8jFTe63wHnAC4CPvuI2sw/wdlKIQUR+BxwLpE6GjsWbiAHcB9wgIqLqJ3rfC8hHirJhLTz2Y0/sIFIJsWZ4+ldw4A+8YMfMayuHegHoQXn7uSSZibvvEORylnluyyew9Mr0TnDplXD0dcH1JzPveMxbLUxVI/z6HZ4101zfDMMwSofkRAjahZW+vbTj9n7Qju3Xu27RJLQNo1TIZTK0SVUfKcK9dwA+SjleAewblEZVYyKyCRgC1BehPMUnn6Bx14V9z8zey2XIZ+HcV7KvtYB0IxeC6pW6udefUBi23wOmP+IJLIQi3kQoZPswG4Zh9Ah+Ak2TF3pucaneKBs/9MQWcm3vbWxh9ANyGb0sFZGr8fYUak6eVNUXi1aqbUBEzgDOABg1alQnqXuQXFfONd4+YAXv9+LveAPQ1FWbbcnb6HF6rL52VK/yqT+hMAwcWbxyGiVHr2ljDSNBv6qzfgJNi6Z4rvYLT2xPl3Rxy6e9t7GF0cfJZTKUtNZMSDmnwJe6eO+VQOqofmTinF+aFSISBgYC6/wyU9VbgFsAJkyY0Dvd6FJRDZY03viRrc70cnqsvgbVq2J7nib3trCVxV5Ln2tjjT5Pv6qzQQJNNbu0xyrn4uJmbbXRD+l0MqSqxZLPfh4YIyI74U16TgJOyUizGDgd+BtwAvBkr40XypcgsYX6N9Njhkzr38iHfEQ8CkU++2sZhmEY+RPUtkerc3dxs7ba6Kd0WrtFZHsR+a2IPJI4Hisi3+zqjVU1BnwHeBT4N3CPqr4qIj8RkWMSyX4LDBGRt4HzgSz57T6Ln0zmsXPaJY2TWv8Na3uujEbvI0iGu5jBsEH7a1ndNQzDKAxBbXvVUM/FbdCO3u+OJjXWVhv9lFzc5OYD84AfJo7fBBbhTVS6hKo+DDycce7HKZ+bgBMzrytpCmVizgxaBLhvGqxY1p6meljCNG5uc32OYrkqbEswbFfLks/+WoZhGEb+OA7U7pYui51U+MwVa6uNfkouk6FaVb1HRC6GNlW3Qkps9x0KbWJODVrc8kn63i4jJ3j7B8w/0szZfY1iuyrkEwxbiLL0hGueYRhGfyIegzWvZm95sP0euSt9Wltt9FNyGc1sFZEheKIJiMh+wKailqq3UkwTc6YJ/MAfZO8fYObsvkEpuSoUoiw94ZpnGIbRn9iyun0iBN7ve071zueKtdVGPyWX5YLz8YQMdhGRZ4CheGIGRibFNDFnujdp3MzZfZVSclUoRFlsnwojg9EXLUk7fv+qo3qoJIbRR4i3+rfV8dbc87C22uin5KIm96KIHAjsBgjwhqrm8Xb1I4ptYs50mzNzdt+klFwVClUW26fCMAyjeIQi/m11KJJfPtZWG/2QwMmQiHwt4KtdRQRV/b8ilal3kRpcHqmAqQ/AhnchUgmtDTB45+KYmJPm7MxYDjNn934K9WyDhA/yEUSwemYYhlH6VA+HU+6FTR+2jz8GjvLOG4bRIR1Zhr7SwXcK2GQoM7h8t6PgoB/AkgvSB47FwMzZfZdCPNsg4YOhu8Pa13MXRLB6ZhiG0TuIN6ePPybf2dMlMoxeQeBkSFWnd2dBeiWZweV7nQyLpmYHm3/r8eKYnc2c3Xfp6rMNEj6Y/oj/+Y7qqNUzwzCM0mbL6uzxx6KpXps/cGTPls0wShxb3u0KmcHlFYNLJ/Dd6N8ECR8EBdlaHTUMw+i9FEJAwTD6KTmKzxu+ZAaXN24oncB3o38TJHwQFGRrddQwei3jFozLOrf89OU9UBKjxyiUgIJh9EPMMtQVMjX5X7rb2+TMNPqNniZov4jq4baPhGEYRl+jenj2+OPrd5iAgmHkwLaoyQGYmhz4B5dXDLFgc6Pn6Uj4wAQRDMMw+hahMGy/hxcjFG/1LELVw73zhmF0iKnJdRW/4PLUY9f19gSygafR3QQJHwSdz0dy2zAMwygtQuFssQRr1w2jU0xNrpgEyRsHyRgbRk9hddUwDKNvYe26YeRETm+DiBwlIheKyI+TP8UuWJ8gSN64YW3PlsswMrG6ahiG0bewdt0wcqLTyZCIzAUmA98FBDgR+ExXbioiNSLymIi8lfg9OCBdXEReSvws7so9e4QgeWOTMTZKDaurhmEYfQtr1w0jJ3KxDB2gqqcBG1T1cmB/YNcu3vci4AlVHQM8kTj2o1FV90r8HNPFe3Y/SXnjVEzG2ChFrK4ahmH0LaxdN4ycyEVmpDHxu0FE6oB1wIgu3vdY4KDE5wXAU8APuphn6ZGUN8701+0FMsbqusTXr0dbWpBolFBNDWI+xn2XyqHolAeIf/w+Gq5CYlsJjRiN9IK6ahiGYfhgYxDDyIlcJkMPicgg4GrgRTwluVu7eN/tVfXjxOfVgI+0FQDlIrIMiAFXqervgzIUkTOAMwBGjRoVlKx76aUyxuq6NL/5FivOnknrylVEdqhj5I1zKNt1jDVGBaLU6qsCzetirDjv5ynP/EbKhni+sYZRanXWMDqj39dZG4MYRk6IqnacQKRMVZuTn4FyoCl5roPrHgf8dvv6IbBAVQelpN2gqllxQyKyg6quFJGdgSeBQ1T1nc7+qAkTJuiyZcs6S2YEEKuv5/3Jk2lduartXGSHOkYvWkS4trYHS9bjFGVeUAr11Z55n6Uk6+zoi5akHb9/1VFdLVLfYtZAn3ObApOPWzAu69zy05cXskTdSUnWWaP76IX9ka0Z9nJysQz9DdgbIDEBahaRF5PnglDVQ4O+E5FPRGSEqn4sIiOANQF5rEz8fldEngI+D3Q6GTK6hra0pDVCAK0rV6EtFnTZV7FnbhiGYZQC1h8Z3U2gvVFEhovIeKBCRD4vInsnfg4CKrt438XA6YnPpwMP+tx/cMIShYjUAhOB17p4XyMHJBolskNd2rnIDnVI1IIu+yr2zA3DMIxSwPojo7vpyPnycOBXwEjgWuCaxM95wCVdvO9VwGEi8hZwaOIYEZkgIsl4pP8AlonIv4CleDFDNhnqBkI1NYy8cU5bY5T01w3V1PRwyYxiYc/cMAzDKAWsPzK6m0A3OVVdACwQkeNV9f5C3lRV1wGH+JxfBnwr8flZINsR2ig64jiU7TqG0YsWmZJLP8GeuWEYhlEKWH9kdDe5xAw9IyK/BepU9QgRGQvsr6q/LXLZjB5EHKdUAxWNImHP3DAMwygFrD8yupNcJkPzEj8/TBy/CSwCbDJkGIZhGL0IP+U56NXqc4ZhGF0iF5tjrareA7gAqhoD4kUtlWEYhmEYhmEYRpHJZTK0VUSG4O3LiIjsBwRveGAYhmEYhmEYhtELyMVN7nw8KexdROQZYChwQlFLZRiGYRiGYRiGUWQ6nQyp6osiciCwG94uu2+oamvRS2YYhmEYhmEYhlFEOp0MiUg5MBP4Ip6r3F9EZK6qNhW7cIZhGIbRK5k10Odc6XqY+wkrmKiCYRj9gVzc5G4HNgP/mzg+BbgDOLFYhTIMwzAMwzAMwyg2uUyG9lDVsSnHS0XktWIVyDAMwzAMwzAMozvIRU3uxYSCHAAisi+wrHhFMgzDMAzDMAzDKD65WIbGA8+KyIeJ41HAGyKyHFBV3bNopTMMwzAMwzAMwygSuUyGJhW9FIZhGEafZ/RFSzo8//5VR3Vncbqfrogq9DJBBsMwjN5CLtLaH3RHQQzDMAzDMAzDMLqTXGKGCo6InCgir4qIKyITOkg3SUTeEJG3ReSi7iyjYRiGYRiGYRh9mx6ZDAGvAF8Dng5KICIh4EbgCGAscLKIjA1KbxiGYRiGYRiGkQ+5xAwVHFX9N4CIdJRsH+BtVX03kfZ3wLGAyXobhmEYhmEYhtFlemQylCM7AB+lHK8A9g1KLCJnAGcAjBo1qmiFcl1l3dYWWmJxouEQQ6qiOE7wpC7f9Eb/oLvqa3+lmO9df32nrc4avY1trbP5vOP9tT0wjL5E0SZDIvI4MNznqx+q6oOFvp+q3gLcAjBhwgQtdP7gNXpvfLKZb9++jBUbGhk5uILfnDaB3bYf4Nv45Zve6D90R33trxTzvevP77TV2SLhpxJnFIRtqbP5vOP9uT0wjL5E0WKGVPVQVd3D5yfXidBKYMeU45GJcz3Guq0tbY0ewIoNjXz79mWs29pSkPSGYXSdYr539k4bRt8mn3fc2gPD6Bv0lIBCLjwPjBGRnUQkCpwELO7JArXE4m2NXpIVGxppicULkt4wjK5TzPfO3mnD6Nvk845be2AYfYOektY+TkRWAPsDS0Tk0cT5OhF5GEBVY8B3gEeBfwP3qOqrPVHeJNFwiJGDK9LOjRxcQTQcKkh6wzC6TjHfO3unDaNvk887bu2BYfQNemQypKoPqOpIVS1T1e1V9fDE+VWqemRKuodVdVdV3UVVf9YTZU1lSFWU35w2oa3xS/oHD6mKFiR9LOayamMjH6zbyqqNjcRiboflcV1l7eZmVm5oYO3mZlzX3PiN/kfmezC4IpLXe5cPHb3T9j4aRu8nn3e8o7bG2gPD6D2UsppcyeE4wm7bD+CBmS8MoQgAACAASURBVBNzVo4pCzv89Ng9qIyGaGiJUxb2n3/GYi6vf7KZGXe+0BaIOXfqeHbffgBhn2sscNMwgt+D6rJQTu/dtuD3Truu8tbaLfY+GkVl3E4ZimgLxhX3fgH5Lz99eVHv29Pk8477tTXWHhhG76KUY4ZKhtQVng2NzcTiLjFXaY27Was9ra1xVm5o4IN1W/l4UyOvrtxAS9yz8LTEXa565N++wZVrtjS3TYTA8zuececLrNnS7FsmC9w0ejtBK6f5nA96D1ZvavZ977q6WrtuawtXPfLvrLzXbGm299Ew+gDrtrbw6soNjNm+muEDyxmzfTWvrtwQ+I77tTUdtQdmMTKM0sMsQ52QuvI8tLqMCyftxvfve9nXetPaGuf1NVs4KzGp+fLYYXz3kF3bjkcOruAXx++J62a7v7XGXd9AzFjc31XOAjeN3kyQRWfM0GrfFdWg8zWVkaz3YGh1GeURh58+9Fraeyd03Zrqui6nH7ATP7j/5ay87X00jN5PJKSMHrodJ93y97Z3/Kap46mISM5tDQHtgeu65tFhGCWIWYY6IXXlecZBu7RNhCDberNmS3PbxAfg+PE7ph2v2NDID+5/mbjPQlAk5PgGYoZD/o/IAjeN3kyQRSdoRTXofFzJeg/OOWQMZy18Meu9a465XbbexJW2iVBq3q5POex9NIzex9ZmN6vfPuvOF2hocXNuazSgPYgrZkE2jBLELEM+pO4oHdf2FZ5BFf6r0K4qH6zbCpD2vV/6pLXng3VbiYQchlZF2dgUw1Xl7m/vx+pNTbiqNLTE2bGmgmHVZb5lTAZ5Zq4wFSJI3DCKTZBlszXuMrS6jB8dPZZBFRE2NrYy96l3iAVYTlU16z0YXVvJATsP4dv/vTMhR4i7ym+efpeYm7/1prU1zpotzcRcJewIISd7dTh5nM/7WKhd6wuVj2H0R2IxlzVbmmmNu0RCDsOqy4i56t8Gucrt39iHD9Y1tMUH7TS0yjct+LcHqmZBNoxSxCZDGWS678yb9gVGDq5gxYZGNja2tn0G+PyOg7hw0m5t5vTUtEBWevBWh1ZtauKkW/7u60Z39Ql78ss/vsHaLc3MnTq+w7LmKs5gGKVG0rKZ+W5UREJZrqhXn7An5RH/9NFwiN22r0gTNXFEmbr/Z5g+//m2POZM2ZtowvqamUck4L3JdHtNusV+eeww/vTamqw8chVXKZT4iYmoGMa2EyRaNGxAtjv81SfsSUUkxLqtLfzowVfazt87Y/+A9sphxMDs9mDd1pbAdswwjJ7DRs8ZZLrvXP/EW1x9wp5eQ/nUO22fwTORp7rNpaYFuP+Fj5gzZe802c2rT9gTVc9Pzs+N7vv3vcyMg3bJSUDhtNueY/r855l8y9+ZPv95TrvtOTO3G72CIPnauKtZrqjfv+9lz7oTIGHrOMLQAWXsMLiSoQPKaGp1mZnhujJz4Ys4Qtr7mXwfwwETh0y31+Q7+cOjxvrmkVmOoAlJocRP+qKIyuiLljD6oiU9XYw+wbgF43x/DI8g0aJY3PVtg2LxbDfbpta4b9rmmPq2B/lut2EYRvfQLy1DHbmWtMTiWS42f1z+MYvO2A+AyqjDojP2I+YqIUc4YOchHDJ2+zYT+bL31rd9H3aE/3thRZoJ/Zd/fIOLjtgdCHajG1QRAdpd8FZuaPAtp5nbjd6C3zu3y5DKtHdlaFWUjzc3+9fruMuYodXcc+b+aS4tjiNZeYvgm0dTzOWXf3wj63284ZTPEyvzd5fxDYJW9c2Dqtz+Fx29u/m4vVkbYBjbTpBLbmuAm5zf+XCA22xrgPDRtmzPYRhG8el3k6HOXEuqykK+LjZVZSGqo5Ess/qcKXtzw5Nv8afX1vDlscP4zpfGMDlFhSbp9vbPjzYC3krQxsZWINiNbmNja5YLXmY5g9yMzNxulBp+79zd396XjY2xNBe0m6aOZ/sBZb71ujzs5Kwyd3OAK1vYEdZuaebMO15Izzvi+LrL1FRFfcsSd8nKI5/3LtBFMBrKy+3N2gDD2HaCXHIrwk6gm5yfmmxQWxNE0mJkGEbp0O/c5NZtbeH3L37EvGlf4MkLDmTetC/wj3fWsvrTJlZuaGBrc9zXxWZrc5xPNjdlmdVnLnyR48fvCHhub5nXfv++lznnkDEAbZOn+1/4CAh2o5v71Ducc8gY5j3zHj86eiyLztiPHx09lusee6PNBcbM7UZnlMp+Fn7uXM0x9VVsirnKTVPHp9Xrm6aOR0RyVpk708eVbc6UvSmPOPz29PE8fv6BPHnBgTx+/oH89vTxNLe6vu4y4ZD4lqUsLF3acT7o3Y25mpfbm7UBhrHtBLnkxpRANzk/NdnMtuamqeMDhY8MwyhN+p1lSFCO+twOaZafm6aOZ9biV/jTa2u4b8b+vmbv5pjb9jnzu6Rb27ABZb7f7zK0ij9//yBCIlz/xFscP35HvvnFndnY2Mqdf/ugzQXPVVi9qYmLjtidEYPKffczSe5RZOZ2oyNKKbjez50r5Pi/S7G4y+7DqtPc54ZVl/HJFn/3uaD9uT5tijFv2hfS1OS+d/iutMSUsxY+1/7uT9kbygPc6lrivmUJhZys9w7I+f8d9O5+vKkxL7c3awMMY9tpCWg7gtqU1gC3WUdg/vR9cMTrw8vCQihgSwzDMEqTfjcZao5lB1efdecL/OjosfzptTWBai/xxCpvkFsbQHVZ2Pf75pjLYdc9zc2njufZd9dxzwsr0r7/ftiLITpuzjNt1z523n/77mdyz5n7t11r5nYjiKDg+gdmTuz2OuPnzhV2/JXdQo4QiYTYYXBlWh6C/7sXdsT3fP3mZqbPfz7tXGtcs/YEOWvhiyw6Yz//vEOOb1mArP/h2s3++yAF/b/93t1tcXuzNqCHmDUw43hTz5SjF+An2rD89OU9UJJ0IgHqkkFtStB5EeHQa59KO9cT7axhGNtOv1u+CAqKTlp35j71Dr84Pl1xas6UvfnN0+/ym6ffzXJru2nq+Da3t5a4m3XtL47fk5ZEMOX9L3yU5XaTdGvJXD3f0hzzLWdSic6PUnGLMnqeUgqu93Pnchx835UgX3sR//SRkGS7ip06gR1rKtLOzZ06PvDdj7vK3Iz3cm6eri6diSJ0xX3O3N4Mo/AMqy7zfe8jIcnq55Nutn5tEBl9somYGEbvo0csQyJyIjAL+A9gH1VdFpDufWAzEAdiqjqhq/cOWg1KWnf++dFGFjz7HovO2I94QjHu9mffS7PmzJ++D5GQEAk51FZGmHXMHlx6lEvIEX79+JtpajMLnn2PWcfswdPfP4hwYpNVP7eWzFXhNZub81olLiW3KKPnKaXgej93LkVZ8Ox7We/Kz47b0zcPQXzTX37MHr6uYq6r3HPm/sTiLuGEOtyaLf7vVDjksPv2A7LSh/PYtyvo/x0JO112n7P31zAKTzjs/95/srmJJf9ameZme9+yDzntgJ1826ALJ/1HWr4mYmIYvY+ecpN7BfgacHMOaQ9W1fqu3CxVrraqLMTcqePTlKNumjqe/33iTcBryM47bDdGDKxok+396t47suSVT1ixoZFn313H9C/uxM617YOZuqj3b1y/tZnpE3fy3YCtZlBFW3mGRrIbyuSqcHLQdP8LH2WVs6NV4lJyizJ6nsz61NNWhkx3LtdVzjtst5zLF42I77sVjYivq5jjCHUp7xy0rwRnbbKYmPhkps+HoP932PEXfsjHfc4wjOLgON6ipqoSCTk4jlAZdTh6r5FZccUDyh3OOWTXrPYjKahSCu2sYRjbRo9MhlT13wAixV/xDJL1TV0NGloV5WfH7cllX8lejc1ntbaxJR64j0lne5D43WdwRSTnVeJScosyep7eYGUoCzv89Ng9qIyGaGiJU9aBJaapJXiPoFz39wlaCc7HAhREoUQRDMPoHoK8KSqjIZ769yfc9e39UFVEhAdfXMGxn9/Bt/1wHCnpdtYwjM4pdQEFBf4kIgrcrKq35JuBn8Xk5N/8gwdmTkxbCfaz1iTJdbU2Gg757mOSq8nc7z65rhKXkluUURqUspVh3dYWTrvtuaz6GmQx6eq7laSrFqCOKJQoQl9j9EVLunzt+1cdVaji5E+mWMK2pulDlKooQj4EeVMsOmM/Fr2wgmsef6st7cjBFRw/YcfA9qNU21nDMHKjaAIKIvK4iLzi83NsHtl8UVX3Bo4AzhaR/+7gfmeIyDIRWbZ27dq289tiMdlWIYKeDIC24OveRVB97S/k+14Wu34XS3ykL72X/b3OGr2PjupsUBtUFna6LKhiGEbvomiWIVU9tAB5rEz8XiMiDwD7AE8HpL0FuAVgwoQJbSOZfFdmuyJE0JOuSb3BLcpoJ6i+9hfyfS+LWb+LKT7Sl97L/l5njd5HR3U2qA3y4oZCaS68ldFQr3xnDcPIjZKV1haRKhEZkPwMfBlPeCEv8l2ZDTKdB+0Cn0nSVWaHwZUMHVDWrQ1oT97bMPJhWywmxarfXX3nO8PeS8MoPYLaoJirnHbbc0yf/zyTb/k70+c/z2m3PVew9sAwjNKjp6S1jwP+FxgKLBGRl1T1cBGpA25V1SOB7YEHEiILYeAuVf1jvvfKd2XWhAgMo/iUksXE3nnD6H+Y6IlhGEl6Sk3uAeABn/OrgCMTn98FPleI++UTSG4Bz4bRPZSKwIO984bRPzHRE8MwoPTV5LqdUtufxTCM4mLvfGHpinpcd/Lv3dM3y/yP1//d7apwN65OXxM8e/hxbZ/H7TQq53ymPfyZtOP5R37QtYJtA34Kc70Raw8Mo/9hk6EMSsl9xzCM4mPvvGEYSaw9MIz+h02GfCgV9x3DMLoHe+cNw0hi7YFh9C9KVk3OMAzDMAzDMAyjmNhkyDAMwzAMwzCMfomo9r2980RkLfABUAvU92BRevL+9rcXnnpVnVToTFPqa1fp6WdeKPrC31Eqf0Op19l8KZX/a0dYGbtGX6izpfz/zcTK2nWKUmeN7qNPTob+v707j5OjrPPA//lWHzOTg0kmkwAhhDOBZQmiyQ/FeATRXQSXqCACBkFcIaBmAXddWPkpKCrqT6KsQkDNcgqLIIY1KB6AIOguCQLhSgLhClGSmUmGHHN0d31/f1R1p4+q6qrpqu7q7s/79erXdFdXP/V01/d5uquPz+SJyCpVndeO2+d9b9x9b5RWud+tcD9a4T7EUTM8ruwjNdPjy74S8WtyRERERETUpngwREREREREbanVD4aub+Pt8763n1a5361wP1rhPsRRMzyu7CM10+PLvlLba+nfDBEREREREblp9U+GiIiIiIiIHPFgiIiIiIiI2hIPhoiIiIiIqC3xYIiIiIiIiNpSSx4MHXfccQqAJ57CPkWC9cpThKdIsGZ5ivAUCdYsTxGeqMm15MFQX19fo7tA5BvrlZoNa5aaDWuWiNy05MEQERERERFRNTwYIiIiIiKitpRsdAconkw1MTA8gNHcKNKJNHo6e2CIv2PnWm4bZVvUmrJmFn1DfcjkMkglUujt6kXSCDa1sc6oXbnVfvHyzmQnTNPEqMnxQUSthwdDVMFUE+u3rseS+5dg085NmD5+Oq5+39WYNXlW1SfAWm4bZVvUmrJmFuu2rsOFD1xYqJGlxyzF7MmzfR8Qsc6oXbnV/kGTDsKL217EkvuXoLerFxe87QJc+silHB9E1JI4k1GFgeGBwpMjAGzauQlL7l+CgeGBSG8bZVvUmvqG+goHQoBVIxc+cCH6hvz/WJp1Ru3Krfb7hvoKy8+ec3bhQKh4HY4PImoVPBiiCqO50cITX96mnZswmhuN9LZRtkWtKZPLONZIxsz4boN1Ru3KrfYz5u5x1Z3u5vggopbGgyGqkE6kMX389JJl08dPRzqRjvS2UbZFrSmVSDnWSMpI+W6DdUbtyq32U8bucTU4Oti242P/i1cWTkTUungwRBV6Ontw9fuuLjwB5r8j3tPZE+lto2yLWlNvVy+WHrO0pEaWHrMUvV29vttgnVG7cqv93q7ewvLla5bjivlXcHwQUcsS1db757nz5s3TVatWNbobTY1pco4kikZZr7UppMmZGaQMpsmVYc2SpximycWmZos/EXr5yhPC7hK1jkhqluqHaXItpBVf0BliBHqXnxorjBoM2kbSSGKv8XvV1AbrjJpdkJr3E0fPMUFE7YIHQy0iLpHWjCluX2Hs+7i0QdRMgtR8GHH0RESthK8MWkRcIq0ZU9y+wtj3cWmDqJkEqfkw4uiJiFpJQw+GRGS5iGwWkaddrl8gIoMi8oR9+nK9+9gs4hJpzZji9hXGvo9LG0TNJEjNhxFHT0TUShr9ydANAI6rss7DqnqkffpqHfrUlOISac2Y4vYVxr6PSxtEzSRIzYcRR09E1EoaejCkqg8B4HdXQhCXSGvGFLevMPZ9XNogaiZBaj6MOHoiolbS8GhtEdkfwC9V9XCH6xYAuAvARgCbAPyrqj7j0s45AM4BgJkzZ8595ZVXIupxfIUZhz2pYxK2jWyzLhtpGIaB4eywr3bLk4qmdE7B4Ohgoe3udDf6h/s9k4yi4CdBqYrQ4jObqV6D1FUYiVZuyzO5DPqG+pA1s0gaSfR29SKVcH43O4R93SrasmbbUb7m88/pCoUhBgwYSCaSJdHYhTnYzCAlKXQmO7Eru6ty7rfHMIBQkkp9zg+xqVlGa5NPjNZucnF/dfA4gP1UdYeIHA/gFwBmOa2oqtcDuB6w/p9A/boYH2ONQvVKIgIQKJnLVBMvbnuxZP2lxyzFsieW4YGND7hejjrJKG4JSs1Sr0GT2fzWoNv+mDVpFjYMbqjY3gF7HIAXBl9wXL/8gMipBpkmV7tmqdl2lK/5H/7lhzj9sNPxlUe+Uqj977z3O8iaWVzy8CWu8/uZvz4Tm3ZuwjEzjsHiIxeXjLOr33c10ok0Fv92cU3jqREpj6xZIvIj1q8MVPVNVd1hn78XQEpE+Fl+yLySiIImczmtf+EDF2LhrIWel6NOMmKC0thElczmtT+cttc/3O97/zFNjtpNvuYXzlpYOBACrNofHBksHAjll7nN7wtnLawYZ0vuX4KN2zfWPJ44LokormL9yZCI7AXgDVVVETkK1sFbf4O71XKqJREFSeZya6s73e15OeokIyYojU1UyWxu+yNrZgMvr1efieIqX/Pd6e6K2u9Kdvme351uv2nnJnQlu1xvH7SPtbZDRBS2Rkdr3wbgTwAOEZGNIvJpEVksIovtVU4G8LSIPAngagCnaqN/5NSCvJKIgiZzua0/ODroeTnqJCMmKI1NVMlsbvsjaSQDL69Xn4niKl/zg6ODFbU/lB3yPb873X76+OkYyg453n4sfay1HSKisDU6Te40Vd1bVVOqOkNVf6Kqy1R1mX39D1T171X1Lar6DlV9tJH9bVVeSURBk7mc1l96zFKsWL/C83LUSUZMUBqbqJLZvPaH0/amdE7xvf+YJkftJl/zK9avwOXzLy+p/e6Obnzz3d/0Nb+vWL+iYpxd/b6rMWPijJrHE8clEcVVw9PkojBv3jxdtWpVo7vRVLxSfqqlw5UnAnkm05UnGRn1S/oKkkbmIpLEmLjXa1Rpcm77w62NIPuvlmTFMB+PGGjLmm1H+bo0TRMmTJhquqbJuaWFGmIgJSlkNANTzZrT5Ko9F0SdJldsLDXLNDnyiWlyTS7Wvxmi+nFLAfOTDleeCOTUVvnlvcbvFd2dcWCq6ZhSxoSx6vwmxAVJi6q2P5y2l0qksPeEvUPtc1CNSMQi8qPWcVotMS7oeOJYIaJmwRmJPPlJh2uGRCAmGUUvyGPcrPujWftNlOdWw2EkxvnZDscKEcUND4bIk990uLgnAjHJKHpBHuNm3R/N2m+iPLcaDiMxzs92OFaIKG54MESe/KbDxT0RiElG0QvyGDfr/mjWfhPludVwGIlxfrbDsUJEccODIfLkJx2uGRKBmGQUvSCPcbPuj2btN1GeWw2HkRjnZzscK0QUN0yTo6oKaXJ2+lutaXJeiUJhJnWNMcnIS2xSjuKqvFa8kgJHs6PoH+4vpMNN6ZyCdDIduAbqne7GNLnWqtl2UFyzhhjoMDownBtGVrNISQqdyU7syu4qpM+ZMGHAgGEYgeu7eFudyc6SFDufbcWmZpkmRz4xTa7JMU2OPDmlyXklAjklCBWnzx0z4xgsPnIxLnzgwor2AISWPsQko/oLUiuZXAYvDL5QUgdLj1mKg7sPxktvvlRTvUW9n6NKqiOKgtMYuWrBVbjuyesKiaBXzL8C33v8e+gb6quaFhp0W5x3iSjuODuRp6CJQNXS5xbOWlh4AVzeXpjpQ0wyqr8gj3nfUF9FHVz4wIXoH+6vud64n4l2cxojFz14UUki6KWPXIqz55xdc1ooxyMRNSN+MkSegiYCVUuf6053e7YXVvoQk4zqL8hjnjWzjuu6LQ9ab9zPRBa/iaD5y7WkhXI8ElEz4idD5CloIlC19LnB0UHX9sJMH2KSUf0FecyTRtJxXbflQeuN+5nI4jcRNH+5lrRQjkciakY8GCJPQROBqqXPrVi/AkuPWerYXpjpQ0wyqr8gj3lvV29FHSw9ZimmdE6pud64n4l2cxojVy24qiQR9Ir5V2D5muU1p4VyPBJRM2KaHFVVa7pXXNLkQkj9ik3KUVwFecwzuQz6hvoKaXK9Xb1IJVKxT5NrMqxZqkiTS0kKGc3AVBNpIw3DMDCcHQ4ldTOE8RibmmWaHPnENLkmx98MNZEIoqJ9KU/PyppZbN61GZlcBqlEZXyyU9pWtctetw2r36aa6Bvq8x0J3m4vqsO4/6aayJpZ5MwcspKFqaZrGwkjgVQiBYUilUghYSQAuNeAW//qne7W7nVCjVd+cGPAGgeGYSCby8KEaR3oFNVnkDESZF2n8ZC/PccKETWDhh4MichyAB8CsFlVD3e4XgB8H8DxAHYBOEtVH69vL+OhWmR1vSJMs2YW67auq4hEnj15tuv/k4mDapGv7R4JG8b9D1IbQbcXl/0Tl35Q+3KqwcvnX46fPvtTnHvEuRjKDeFLf/xSXerTazwA4f2rBCKiKDV6RroBwHEe138QwCz7dA6Aa+vQp1iqFlldrwhTt0jkvqG+SLdbq2qRr+0eCRvG/Q9SG2FEtjdi/8SlH9S+nGrwK498BQtnLcTAyEDhQCh/XZT16TUeOFaIqFk09GBIVR8C4DUzLgRwk1r+DGCSiOxdn97Fi9941KgjTDO5jGM/MmYm0u3Wqlrka7tHwoZx/4PURliR7fXeP3HpB7Uvr+eCrmRXXevTazxwrBBRs2j0J0PV7APgtaLLG+1lbcdvPGrUEaapRMqxHykjFel2a1Ut8rXdI2HDuP9BaiOsyPZ675+49IPal9dzwVB2qK716TUeOFaIqFnE/WDINxE5R0RWiciqLVu2NLo7oasWWV2vCFO3SOR6/oB9LKpFvtY7EjZu9RrG/Q9SG2FEtjcisjcu/WiEuNVsu3KqwcvnX44V61egp6MHX3/X1+tWn17jIQ5jhTVLRH74itYWkckApgMYAvCyqpqhdUBkfwC/dAlQuA7Ag6p6m315LYAFqvpXrzZbNfa1Wppcd7obg8ODSIx0QExBKpXCuIlpiBE89TGbzWBo6wDMXA5GIgFM7MT27M7CdvqH+5ExM0gZlWly9RQkrajauj7aik3kaxSymVEMbdta2OddkyYjmXJ+F9ctFtttuZPyx3tyejKGd2RhZk0YSaOiduOSTBWXfvjU0jU7FmqayA0MQEdHIek0Ej09EKO++09Nxa7to661Xk2+Bg1TIMNZaC4HSSagHUmMqPU1NIXuTpozjMj+TcFYr/MQm5pltDb5xGjtJuf6ClZEugF8FsBpANIAtgDoBLCniPwZwDWq+kDE/bsHwOdE5HYAbwcwWO1AqJV5RVZnzSxeHXwVOtCBR5avw/b+YUyc0onjz5uDKdMnBHqizWYzGHj1Zay46pt4c8tm7DF1GhZedAlWDt6PO1/8eWwSgYIme1WLl613RHOcZLMZDLz2SsU+75m5P5LJ0oOZTC6D9dvWV6TGHdx9MF5686Ux7Q81Ff2bduDea9e41m5c9k9c+kHBqWliZN16bPzs+ci8vgmpfaZjxg+vQcfsWXU7IPJT69UYYmByehI2v/oyfvndbxTG7AlfuATXvX4z/vy3/w0tbbTaPOs1HjhWiKgZeM2Kd8L6vc67VfUQVX2Xqs5T1X0BXAlgoYh8upaNi8htAP4E4BAR2SginxaRxSKy2F7lXgAbALwA4EcAzq9le62sb6gPA9vexCPLX8H2/mEAwPb+Ydx77Rrs2h7sB6tDWwcKL4oB4M0tm7Hiqm/ilH1OjFUiENOKwuO2z4e2Vj6Wbqlx/cP9Y94fu7aPFl4cAmOvXSIvuYGBwoEQAGRe34SNnz0fuYH6zRlh1fr2wYHCgRBgjdmV3/0mzp11dqhpo5xniajVuX4ypKof8LhuNYDVtW5cVU+rcr3C+nSKqsjkMphgTMT2/tInqO39wzCz1b8KWczM5QpPsHlvbtkMNa124pIIxLSi8LjtczOXq1g3a2YdH3e35X72h5k1Cy8O88ZSu0RedHS0cCCUl3l9E3S0fnNGWLVuZp3HbEqtp/Ww0kY5zxJRq/P1ebmIHCEiJ4rIR/OnqDtGwaQSKewwt2PilM6S5ROndMJIBvs6q5FIYI+p00qW7TF1WuErHHFJBGJaUXjc9rmRSFSsmzSSjo+723I/+8NIGqHULpEXSaeR2qe0RlP7TIek6zdnhFXrRtJ5zGYkCyC8tFHOs0TU6qr/klFkOYDlAE4C8E/26UMR94sC6u3qRc+kPTD/7P0KT7T576KPmxjsSatrcg8WXnRJ4Yk2//uRO16/J1bpWXFIK2oVbvu8a3LlY+mWGjelc8qY98e4iWkcf96cmmuXyEuipwczfnhN4YAo/5uhRE/95oywan1idw8+9IX/KBmzJ3zhEly3Ef9hjwAAIABJREFUfnmoaaOcZ4mo1VVNkxORZ1X1sDr1JxTNnHRUi6yZxbahbXaanAFJKEaSO2CIYlJnL7ZlBl2T6Mov75GYgJHBwUKymDm+A29mtyOVSGFK5xQMjg7WksrmW7UEvfLLESd7xSblKApB0uRGs6PoH+4vpMZN6ZyCdDKNrJlF31BfRdJgYXkug1TCWm6aZmkbHVOQ2aXIZU2oYWIkvbMkBcvMZTEwtAWjZhZpI4merqkwEo1JMWwiLV2zYxGvNDmFkRTfaXLZbAZ9w1ZaY8JIosPogAxlgJwJI5nEaIdiODdSGJNvZt4smRtNNQvjMGkkkTbSUNHCgY3bXNuZ7IRpmhg16zLvxqZmmSZHPvErDE3OzyuJP4nIYar6bOS9oZokjSR6x/fC7Mxi/dZ1WPL70rSv4mQhX5efWob+4X5c8LYLcOm9l7q2VZwsFDThzYtTW2ElJFEpU028uP0lLPlD9f1mqumYGnfQpIPw4rYXK5Yf2H1gRfrczR+8GX3DfRWJdAd1H4SNb76KJb8va3uPA/HitvVY8uDu9a9esBSzJs/mAREFIoaBZG9jE87EEIzv7gh0m2w2g3WDpePo8vmX46fP/hT/MvdfkDWzWPIr9/GbNbNYt3Vdye2vWnAVfr3h1/joIR/FaG60MHaPmXEMFh+5uGTdfHsAQpvjiYjiwM/MdROsA6K1IvKUiKwRkaei7hiN3cDQlsKLRmB32ldxspDfy2fPORuXPnKpZ1vFyUJhJg85tRVWQhKVCrLf3NbtG+pzXV6ePpfVrGMinWvbw30VNb3kwQsxMMR/pEjtofjNA8AaA1955CtYOGshNu3YVHX8Oo3Dix68CB+e/WFs3L6x5PYLZy2sWDffHtPliKjV+HlL9ScAzgCwBkBo/2yVojPqkupVnizk97KftvLJQmEmD7m1FUZCEpUKst/c1s3kMr5T5nJmLlAiXcYtqc7M+ruDRE3ObQx4zdPF49dtfCYkga5kV8l13eluz/aYLkdErcTPJ0NbVPUeVX1JVV/JnyLvGY1Z2iXVqzxZyM/lwdFBX23lk4XCTB5yayuMhCQqFWS/ua2bSqR8p8wljESgRLqUW1Kdwa/IUXtwGwODo4MYyg5VHb9u4zOnuYrbu8376USa6XJE1HL8HAz9RUR+KiKnMVq7OfR0TcXVCyrTvoqThfxeXr5mOa6Yf4VnW8XJQmEmDzm1FVZCEpUKst/c1u3t6nVdXp4+l5SkYyKda9udvRU1ffWCpejpmhrtA0MUE72dlePo8vmXY8X6FZg+YXrV8es0Dq9acBV+se4XmDFxRsntV6xfUbFuvj2myxFRq/GTJvdfDotVVc+Opku1q2fSUZjJaaH2qyx5K2iaXHe6G/3D/ciYGXQmrAjYjJnxlSQUZupXtTS5Oj/esUk5ioJbEpwTt7p3W+7Utmnm0D88UJRI14N0ssO9babJjUVL12wr8npOKaTJaRYJSUJEAFX0IgFDDAwIColvTnNj8ThMiv80ufL2In7ei03NMk2OfGKaXJOr+kpCVT9Vj440ozCT08JmJJLonbB3ybLeZGmCUm+X9+W9xu/l2n75ugWmCWPL8+i9/TRg26vApJnAqbcB0w4DxhBfa4hRtZ9UO1NNxyQ4t1p22i9ey5NGsrSeTBPoex57O9SJYbi07VDTRK2k2nNKMpnCXvkxYJrA5meBojHUW2WurRiHZfzOtW7jnIioGfn5p6s3isikosuT7X/E2vaYquNg15bdT86A9ff206zlFFt1r2XWCVGFQOOQY4iIKBR+3qo/QlW35S+o6lYAb42uS80jzOS0lpEd3f3knLftVWs5xVbda5l1QlQh0DjkGCIiCoWfgyFDRCbnL4hID/xFcrc8puo4SKatrzwVmzTTWk6xVfdaZp0QVQg0DjmGiIhC4edg6Luw/unq10TkawAeBfDtaLvVHJiq42DcVOu3H/kn6fxvQcYx9SvO6l7LrBOiCoHGIccQEVEo/AQo3CQiqwC8z170UVV9NtpuNQdDDMyaPAu3nnBr7NLkKuSywI6/AbkMkEgBE/YCvJK4TNP67nl21HqncdxUfwEIhmH9gPeffxf8tk7diGlaX6sJXMtjrY/CBg2YUw/FwD//pjQdbix14tQXoLb+ETWAIQZmdR+EW4+7Yfe46OyFsdOhlsvn2lQXYOaAN18fU81zriWiduX6alhEJqjqDgCwD34qDoCK1xkLETkOwPcBJAD8WFWvLLv+LADfAfC6vegHqvrjsW4vCk2RqpPLAm88Ddxxxu7krlNuBvY83PmAyCGlKFAinGEAE/asudtxTutrRb5rudb6gL1vB/2n1wXqy6K7gexwTf0jaginNM5Tbgb+8G1g7crKWs7PtTWOSc61RNTOvGa5FSLyXRF5j4iMzy8UkQNF5NMich+A48a6YRFJAPghgA8COAzAaSJymMOq/62qR9qnWB0INY0df9t9IARYf+84w1ruJCYpRUzri6kQ6iO0fevUl60bYlG/RIE51fMdZwBHnrb7slMt1zgmOdcSUTtz/WRIVY8VkeMBnAtgvh2ckAGwFsBKAGeqqsuraV+OAvCCqm4AABG5HcBCOHwCRTXKZZxTh3IZ5/VjklLEtL6YCqE+Qtu3Tn1JjYtF/RIF5ja2uiaXXi6v5RrHJOdaImpnnp9/q+q9qvoJVd1fVfdQ1Smq+k5V/XqNB0IAsA+A14oub7SXlTtJRJ4SkTtFZN8at9meEinn1KFEynn9mKQUMa0vpkKoj9D2rVNfMrtiUb9EgbmNraGtpZfLa7nGMcm5lojaWdy/DPw/APZX1SMA/BbAjW4risg5IrJKRFZt2cKvw5SYsJf1vfPi1KFTbraWO4lJSlErp/U1db2GUB+h7Vunvkw+MBb122qaumabhVM9n3Iz8MRtuy871XKNY7JV51rWLBH5IaramA2LHA3gMlX9R/vyJQCgqt90WT8BYEBVu6u1PW/ePF21alWY3W1+9UqTC1nMEo4kikabsl5DqI/Q9i3T5LywZptNeT13TQGG+qvXco1jMkZzbWxqdv+LVxbOv3zlCWF3iVpHJDVL9dPIf576GIBZInIArLS4UwGcXryCiOytqn+1L54I4Ln6drHOqj2ZBXmyK1vX7OzBQCKBUVGkjQR6UPaxoNP6MDFqAGmY6BneBmN0p78n5xDvR1Ok9cWAmctiYGhLaUy118GuYyMB6sslMdC1Hw5tG2YOvSNDgJkFjByQygFJw/3A3a1/bumFISQaEkXCa6yZGWu5mQWyAEZ3Vv6+0x4jJoABw8CoAJ3JTphGAqPJBNKJBHok2Fc/ONcSUbvy9WrJ/lRmz+L1VfVV91tUp6pZEfkcgPtgRWsvV9VnROSrAFap6j0AlojIibCeEgYAnFXLNmOtWjRqkOjUsnXNo5dg/dxTseTBi3bHpi5YilmTZ+9+oVp1/e9i1iPXwti1FXjvF0tjuoP0M4RYZipl5rJYv3Udljx4ofP+9dVICHHZbv2YNAtG39qy+OufA6O7gDsWlUYIT/s7YPNzlTHw0/4eKG+DdUPNyGusmZnS+j/kBIf59qeAkYJ5/9ew/l2fxZI/X47erl5c8LYLcOkjlzIam4gooKqzpIh8HsAbsH6zs9I+/TKMjdsBDbNV9SBV/bq97Mv2gRBU9RJV/XtVfYuqHqOqz4ex3ViqFo0aJDq1bN2B/+eThQMbwI5NffBCDAw5t+28/hcw8O4LrIjX8pjuIP2MSWx3KxkY2lI4AAEc9q8fYcRle/WjIv76pd0HQvlld5wB7NjsHgPPuqFW4DXWyuvfcb49HRh8FQNv+wSW/PlybNq5CWfPObtwIAQwGpuIKAg/bxv/C4BDVLU/6s60tWrRqEGiU8vWHTWSzrGpZjbY+kbSinitpZ8xie1uJaNm1nv/+hFGXLZbPzTrP/7adFjXaznrhpqN11grr3O3+TY1DqPJ3fN0d7qb0dhERGPk5/Pz1wAMRt2RtlctGjVIdGrZumkz6xybaiSDrW9mrYjXWvoZk9juVpI2kt77148w4rLd+iFJ//HXhsO6XstZN9RsvMZaeZ27zbeZXUjvGiiMt8HRQUZjExGNkevBkIhcJCIXAdgA4EERuSS/zF5OYaoWjRokOrVs3Z7HbsLVC64qjU1dsBQ9Xc5tO6//XfQ8/D0r4rU8pjtIP2MS291Kerqm4uoFS933rx9hxGV79aMi/voA4JRbHOLep7nHwLNuqBV4jbXy+necb38KdM9Ez+O34up3fAXTx0/H8jXLccX8K1ouGpuIqB5co7VF5Cset1NV/Wo0XapdrGNfPVOEIk6TG+4rpHxN6uzBtqF+jGoWaUmip6sXxvBW1/V7jA73NLnyy509wM43dqeBjd8TGB5wXj+RBowEkBlqhgjk2ES+losyTc4xclfhvG42Y/WjUFdTYSRTzglxmrN+I2FmrXfEJ0wDkh1ANmOtW1i+F+DWRtD72H5iW7MtL8hcXzxndky05kMRQBVQExDDugxYYyQzBEBgJlJW6mc+Tc40MWpWj8aOUYy2k9jULKO1ySdGazc511cSqno5AIjIx1T1Z8XXicjHou5YS6qW2OUWEZxX7XqPdQ0AvRP2trqRzWD9tnVlaXFXYdak2dYL17L1C8YVvcuYb9vpPp1yM/CHbwNrV7qkIdn3GWCyXEiMRLJyfwVupLK+TDWxfut6LLl/SWlKlZmAcctHSvfb1ENh9K1Fb/n+nHoosOX50v185kpgaMB/apxTG6wViqsgc30uC7zxdOlYOP1nVrLc7afvXvbhZUCqC/jZmYVlxqm3oTfgGHAd00yeI6I25Wfmu8TnMqomJklqVupXeVrcRcHSx/Kc7tMdZ1gpSIB3+lxMHg9yNzA8UHjRBBSlVA2+Urnf3BLfnJZnh4OlxjFNjppJkLltx98qx8Lgq7sPhPLLfrEY2NVX8xhwHdNMniOiNuX6yZCIfBDA8QD2EZGri67aA9b//aGgYpKkNqoeqV9Bud2nrsnW+WrpczF4PMjdaG7UuVY6xpeuuO1V6ys+TvvTablIsNQ4t7ZZKxRHQeZ6p9p2S1tMjfPXpgfXMc3kOSJqU16fDG0CsBrAsP03f7oHwD9G37UWFJMktbR4pH4F5XafhrZa573S52LyeJC7dCLtXCsjO0tXnDTT+h2P0/50Wq4aLDXOrW3WCsVRkLnNqbbd0hYzu/y16cF1TDN5jojalOvBkKo+qao3ADhYVW8sOv1cVbfWr4stJCZJalbqV3la3FXB0sfynO7TKTdbKUiAd/pcTB4PctfT2YOr33d1ZUpV936V+80t8c1pebIzWGoc0+SomQSZ2ybsVTkWuu3UuOJlH14GjOuteQy4jmkmzxFRm/JKk1sDwPlKAKp6RFSdqlWsk46CJMKVp2eNn1qWvjUV2LGlMnXLTzeyoxgY6itK/ZoCY3jb2BLfKvrpkR4XNEEvXmKTcuRL0PS1IGlypuncdnYU2PFGUU3uabWVHS6r1akAEs6pcW410Vy1EhfNVbPNxk9iHATIjuxOhzNz1pgxElbtlyfGqVqJcrlR63aaAyRhXW8krLZzmZrGANPk/GGaHPnENLkm5/W9qA/Zfz9r/73Z/rsIHgdJVIXfRLjyhKHyVLajlwBzTnJO46p2QGSaMPrW7U79ckp8K06EmzQTWHgN8PvLrIOx4lQk0/SX8uV2n4Mk5JF/TglVp9wM7Hm48wGRR/qVYRjo7eotXddpn/fOttqoqMlDgc3PVy7vmgzc+CHnunGqCdYKxYmfxLjOHmDzM9Zc+vZzgXs+t3vdj1xvvdF051m7l534A+B/rwPe+fnK60JMTzSkbEwTEbUxr6/JvaKqrwD4gKp+UVXX2Kd/B/AP9etimypPGCpPZXvrJ9zTuKopTzpySnwrToTb9iqw4nxg/gWVCUZMhIsnp4Qqr/oIsh/d1t3xhss2tzgvz46wbqh5+Rkz+XF45Gm7D4Ty6959jvWJefGyez5nret0HccHEVEk/LzFJCIyv+jCO33ejmpRnjBUnspmJNzTuKopTzpyS3zLJ8KVXy5OMIpJQh6V8Up2cxJkP7qt65YE57ZcpHIZ64aahZ8xk699tznWKR0uv24IyXFERFSdn4OaTwO4RkReFpFXAFwD4Oxou0UVCUPlqWxmzj2Nq5rypCO3xLehrc6XixOMmAgXT17Jbk6C7Ee3dd2S4NyWl/9ekXVDzcTPmMnXvtsc65QOl183hOQ4IiKqrurBkKquVtW3AHgLgCNU9UhVfTz6rrW58oSh8lS2v9zqnsZVTXnSkVPiW3EiXP43Q498rzLBiIlw8eSUUOVVH0H2o9u6E/Z02eZU5+XJDtYNNS8/YyY/Dp+4zfo9UPG6H7neCpcpXnbiD6x1na7j+CAiioRXmtwiVb1FRC5yul5Vr6p54yLHAfg+gASAH6vqlWXXdwC4CcBcAP0APq6qL1drt2WSjrKZsrSt8vQ4h8s7+/ylhwVJgKuWJtc+KV+xSTnyJaQ0Ocd2gIBpciNlSYjTrISsIP2jsWiumm02Y0mT05w1p0Ks8+VpcoD1FbniJLn8eq09v+bFpmaZJkc+MU2uyXm98sj/i/mJUWxYRBIAfgjgAwA2AnhMRO5R1WeLVvs0gK2qerCInArgWwA+HkV/Ysc0gb617klFTklG5QlwbulhtSbAlWPKVzwlkkD3DP/rO+1Ht1S6rh7gxhNK62fqoUDfOue6SnYAk/bd3W61JC6iZuA29+Xr+4FvVKbILbobGNnhPXfnxwLAcUJEFDGvNLnr7LPfUtXLy08hbPsoAC+o6gZVHQVwO4CFZessBHCjff5OAMeKlP/qukVVSypyur48Ac4tPYwJcOSXWypddtghTe5vtSfSsQapFeTr2ylFbuuG6nN3fixwnBARRc7PW0tPi8gjInKliJwgIt0hbXsfAK8VXd5oL3NcR1WzAAYBTHFqTETOEZFVIrJqy5YWeKKollTkdn15ApxTehgT4BquaerVLZXOKQnObd0giXSswdhqmpqNg3x9O6XIpcb5m7uzoxwnNWLNEpEffgIUDgZwGoA1AE4A8KSIPBF1x4JS1etVdZ6qzps6tQV+ZFotqcjt+vIEOKf0MCbANVzT1KtbKp1TEpzbukES6ViDsdU0NRsH+fp2SpHL7PI3dyfTHCc1Ys0SkR9VD4ZEZAaA+QDeDeCtAJ4B8N8hbPt1AEU/IsAMe5njOiKSBNANK0ih9VVLKnK6vjwBzi09jAlw5JdbKl2y0yFNbq/aE+lYg9QK8vXtlCI3+cDqc3d+LHCcEBFFzjVNrrCCiAngMQDfUNUVoW3YOrhZB+BYWAc9jwE4XVWfKVrnswDmqOpiO0Dho6p6SrW2Y5V0VEvSWpDEt2Qa6OwBdr7hL52rfRLgwhSblKOaBEmNSySdl4vh3EaQumIN1kNr1GzUwqrF4nZSXVYiHNT+JFWtIJH8gUzx9srncqdUuvYZJ7GpWabJkU/t8Vv2FuYnx/atAN4F4HQRuRjAegB/UNWf1LJhVc2KyOcA3AcrWnu5qj4jIl8FsEpV7wHwEwA3i8gLAAYAnFrLNuuulsQsp8Q3t8Sh4rb8pocxAa49udVk7yHA5mcqU+PyaYROdeVUP0HqijVIcRBWsqFTOwuvAX5/mRUrX95mee27jQWOEyKiSPn5zdCTsBLd/gvA/QDeC+DLYWxcVe9V1dmqepCqft1e9mX7QAiqOqyqH1PVg1X1KFXdEMZ266aWJCA/aXFMFaKg3GrSLTXOKY2QqJWEldjm1M6K84H5F3C+JiKKsaqfDInIKgAdAB4F8DCA96jqK1F3rCXUkgTkNy2OqUIUhFtdmVnn5U5phEStJKzEtmpzNudrirPLHIKCLxusfz+IGsDPdwA+qKpzVPVcVb2FB0IB1JIE5DctjqlCFIRbXRlJ5+VOaYRErSSsxLZqczbnayKiWPLzNTl+rj9WtSQB+UmLY6oQBeVWk26pcU5phEStJKzENqd2Fl4DPPI9ztdERDHmJ0CBxsowrB/M/vPvgicBOd22awrwT0uBD36rXVKFKGxeNbnn4cCnfuUvjZCoVdQyT3u1k0gDRgI4+QbO10REMcZXOrUqjz11ircOKwlIylKFTBPY8QbjWVtR0P3ltD7g3IaaVn2aWSAn1mUY7qlxQdomakZeiW0V/+JgGrBzs3fU/B77VM69O94ANGfFbHPMEBHFhuvBkIh81OuGqvrz8LvTZMqjVI9eAsw5yT2euJa2y+Nena53i94GwomOpfoIGvXrtP6iu4HscPAIbb99SXYCt3yE9UStLZcF3ni6crwUz7On/wwwM8Dtp1eOB8AaPw98A3j7ucA9n+OYISKKGa9Z+J88Th+KvmtNoDxK9a2fCC+euFrca5Do7bCiY6k+gu4vp/W3bggnQtutL1s3sJ6o9bmNl+J5dvDV3QdC+WXlc++Rp+0+ECpfh4iIGsr14wpV/VQ9O9KUyqNUjUR48cTV4l6DRm+HER1L9RE06tdp/dS4cCK03fqSGue/f0TNKpepPs+6jbXiubdrMudgIqKY8vX5vIicICJfFJEv509Rd6wplEepmrnw4omrxb0Gid4OKzqW6iPo/nJaP7MrnAhtt75kdvnvH1GzSqSqz7NuY6147h3ayjmYiCimqh4MicgyAB8H8HkAAuBjAPaLuF/NoTxK9S+3hhdPXC3uNUj0dljRsVQfQfeX0/qTDwwnQtutL5MPZD1R63MbL8XzbPdM4NSfOo+H/Ph54jbgxB9wzBARxZCoqvcKIk+p6hFFfycA+JWqvrs+XQxu3rx5umrVqvpszE+a3FjjiaslipVfzzS5qEkUjTrWa5RpcuXpWNVqlGlyzax+NduqxpIm5zT3mibT5PyJTc3uf/HKwvmXrzwh7C7Fy2XdDssG69+P5hRJzVL9+HmVPmT/3SUi0wH0A9g7ui41GadIVqd44rDarna92/rV2qJ4Cbq/3NZ3WuYWoR1G20Stxmm8OI0fzr1Evs25cY7rdWvOXFPHnhD5Oxj6pYhMAvAdAI8DUAA/jrRXREREREREEfNzMPRtVR0BcJeI/BJAJ4DhaLtFREREREQULT9fWP5T/oyqjqjqYPGysRCRHhH5rYist/9OdlkvJyJP2Kd7atkmERERERFRMddPhkRkLwD7AOgSkbdi9w/E9gAwzu12Pl0M4PeqeqWIXGxf/neH9YZU9cgat0VERERERFTB62ty/wjgLAAzAFxVtPxNAP9R43YXAlhgn78RwINwPhgiIiIiojhiCh21ANeDIVW9EcCNInKSqt4V8nb3VNW/2uf/BsAtaqdTRFYByAK4UlV/EXI/iIiIiIioTfkJUHhERH4CYLqqflBEDgNwtKr+xOtGIvI7AE7/yfFLxRdUVUXE7Z8d7aeqr4vIgQDuF5E1qvqiy/bOAXAOAMycOdNpFaLYYL1Ss2HNUrNhzRKRH34CFP4LwH0AptuX1wG4oNqNVPX9qnq4w2kFgDdEZG8AsP9udmnjdfvvBlhfpXurx/auV9V5qjpv6lT+V2+KN9YrNRvWLDUb1iwR+eHnYKhXVe8AYAKAqmYB5Grc7j0AzrTPnwlgRfkKIjJZRDrs870A5gN4tsbtEhERERERAfB3MLRTRKbA+merEJF3AKj113FXAviAiKwH8H77MkRknojk/6Hr3wFYJSJPAngA1m+GeDBERERERESh8POboYtgfZJzkIg8AmAqgJNr2aiq9gM41mH5KgD/bJ9/FMCcWrZDRERERETkpurBkKo+LiLvBXAIrP81tFZVM5H3jIiIiIiIKEJVD4ZEpBPA+QDeBeurcg+LyDJVHY66c0RERERERFHx8zW5mwBsB/Cf9uXTAdwM4GNRdYqIiIiIiChqfg6GDlfVw4ouPyAiDDIgIiIiIqKm5udg6HEReYeq/hkAROTtAFZF2y0iIiIiovDNudE5n2vNmWvq3BOKAz8HQ3MBPCoir9qXZwJYKyJrAKiqHhFZ75qQaSr6d45iNJtDOpnAlPFpGIY0ultEBI7POOI+ISKiRvJzMHRc5L1oEaapWPvGdnzmplXYuHUIMyZ34UefnIdD9pzIJ3eiBuP4jB/uEyIiarSq/3RVVV/xOtWjk82if+do4UkdADZuHcJnblqF/p2jDe4ZEXF8xg/3CRERNVrVgyHybzSbKzyp523cOoTRbK5BPSKiPI7P+OE+ISKiRvPzNTnyKZ1MYMbkrpIn9xmTu5BOJhrYKyICOD7jiPuEmsX+F68snH/5yhMCX19Xl3WXXR5sbDtEMcdPhkI0ZXwaP/rkPMyY3AUAhe+/TxmfbnDPiIjjM364T4iIqNH4yVCIDENwyJ4Tcff585mMRBQzHJ/xw31CRESNxoOhkBmGYOrEjsJl01Rs2T7CJ3qiiASJZi4fn9R4xfuEMdtERFRvPBiKEGNjiaLFMdY6uC+JiKgR+JuhCDE2lihaHGOtg/uSiIgaoSGfDInIxwBcBuDvABylqqtc1jsOwPcBJAD8WFWvrFsnQ8DYWKJocYy1Du5LIvIy58Y5gdZfc+aa0LYxlraoeTTqk6GnAXwUwENuK4hIAsAPAXwQwGEAThORw+rTvXDkY2OLMTaWKDwcY62D+5KIiBqhIQdDqvqcqq6tstpRAF5Q1Q2qOgrgdgALo+9deBgbSxQtjrHWwX1JRESNEOcAhX0AvFZ0eSOAt7utLCLnADgHAGbOnBltzzyUpyEdNGUc/vucdyBrKpKGYNqEDtcfA2cyOWzeMVKybirl/q4ok5eaV1zqtdm5RTObpuJvbw4jkzORShiYNqEDyWTw936cxhgAx3GXzZrYvGOk5m3W2r+o5oBaatapn7mcWTLfJQzBlPFp/Py8d2I4k0MyYWDq+DTnOBozzrNE5EdkB0Mi8jsAezlc9SVVXRH29lT1egDXA8C8efM07Pb9KE9D+ofDpuHzx87GebesLqQjLVs0F4fuObHiRVImk8PEGom0AAAaA0lEQVTzm3eUrHvtork4dNoExwMiJi81tzjUa6soj8vOZk08/8Z2LPYx7rw4jbGbzj4KI1mzYtwd3DseazfvqHmbQdR7DhhrzTr187bPvB3bhrIl8923TjoCNz76Ej41/wB8+9drMXViGkuOnV3ymHKOoyA4zxKRH5G9bamq71fVwx1Ofg+EXgewb9HlGfay2CpPQzpp7r6FJ3vA+jHw4ltWY/OOkYrbbt4xUrHueS7rOm2LyUtEls07RgovoAHvcefFaYy90r/LcdyFtc1a+xfHOcCpnyNZrZjv/v2up3DS3H3xb3c+hcULDsJJc/eteEzjeP+o+e1/8crCqW1c1l16ipE5N85xPBFFJc7R2o8BmCUiB4hIGsCpAO5pcJ88lachTepKOaYjZXNmxW2zpjqvazq/mcXkJSJnmZzpe9x5cRpj49IJ13EaxjZr7V8c5wCnfhoCx77n58xJXSnX+TNu94+IiJpbQw6GROQjIrIRwNEAVorIffby6SJyLwCoahbA5wDcB+A5AHeo6jON6K9f5WlI24YyjulIyUTlw540xHldl6+DMHmJyFkqYfged16cxtiu0ZzrOA1jm7X2L45zgFM/TYVj3/Nz5rahjOv8Gbf7R0REza1RaXJ3q+oMVe1Q1T1V9R/t5ZtU9fii9e5V1dmqepCqfr0RfQ2iPA3prtWv4dpFc0vSkZYtmotpEzoqbjttQkfFute6rOu0LSYvEVmmTejAMp/jzovTGNtvyjjHcRfWNmvtXxznAKd+diSlYr771klH4K7Vr+E7Jx+BZQ++iLtWv1bxmMbx/hERUXOLc5pc3ZQnHU3uSmHrUMYxwcgrvckwBLOmTsAd5x5dSJTq6UyVpMlNHZ92/EF1KpXAodMmVCTPuaXJOW3LK6mO2k/c0waj6l8yaeAQh7GUTBqBEt/ckupyOdNxnB6650Tcce7RyOZMJOuQJufWvzjtY8Dq58G94wuPWWfSgKlAd1cS/33OO2CI9UlRwhBc8WHrdwHfO/VIpOw0ubjfPyIiam5tfzDklHS0bNFcXP37dfjNs5tLEowAeKY3maZi/ZYdJddfu2gu/rOoLa+EqVQqgX0mj/Pd7/JtMWmJ8uKeNhhl/0xT8ULfzlAS35yS6rzamD6py7GdqJT3L46KH7OpEzrwxeMOwb/d+VRFityFHzgEHUkDn1z+f7GsWSIiak1xDlCoC6eko8W3rMZJc/ctXM4nGFVLb3K6/ryytsJKmGqWJClqjLjXR5T9c2s7jMS3RqTGNbvix2zxgoMKB0JAaYrcZ25ahVf6d8W2ZomIqDW1/SdDbolMk7pSJZfzCUZe6UZ+2wojYapZkqSoMeJeH1H2z63tMBLfwkqqayfFj5lbQlx++bh0ouK6uNQsERG1prb/ZMgtkWnbUKbkcjqZqJre5LetMBKmmiVJihoj7vURZf/c2g4j8S2spLp2UvyYuSXE5ZfvGs1VXBeXmiUiotbU9s/gTklHyxbNxV2rXytczicYVUtvcrr+2rK2wkqYapYkKWqMuNdHlP1zazuMxLdGpMY1u+LHbNmDL+I7Jx/hmCL3o0/Ow35TxsW2ZomIqDWJqvM/9Wxm8+bN01WrVrlen8nksHnHSEnC2+BIrpBYtEc6gb5do4Xrp3SlMTCcQSZnYkJnAsOjpuN1+fS4/qHS25ZfLl5/6vg0tg1nMZrNIWW/uzyUsc73jkuhb1fGNfkqSAoehSKSB7NavY5VnNLknPqSzeawZedoyThMp5MV4zOf1jYyki0Zl73j0ujoSDq2PTqaqxh3nZ1J1zbcUuaclquqY/+C3He3/RDBPqtbzTr13TStx2p8WrBjxJo3J3YmMFQ0hxoGYJoopMpN6DCwbShXMT82uoapbuo+z+5/8crC+ZevPMFxebHidaq10RCXdZddHqy+jq92fbbjtF6ZOTfOCb79gNacuSa07Xu1hYhqluqn7X4zlMnk8PzmHTivKA3q2kVzcei0CUilOlyv/8/fr8OW7aMlSUj/cNg0fP7Y2YV1z333/vjQkTMcb5tPk7t20Vz88omNuO7hlytuP2NyF75z8hH49q/X4qj9J1W0VZ58VZwkFff0MKq/uCSNOdXmbZ95O7YNZSvGyqwp47G+f2eg5S9t3VXS9vVnzEUqaeBT//WYrzZm947H+r6dFQlxs6eOx7otlcvHpRO+E8+CjMtmHsNOfb/p7KOwazSHVS/1Ye4BvTjvltV454FTsOjo/XD+rY9XpMmd+c4DcOOjL+Hzx87Gc69vw++e34wlx84uefyb5fEgipWxHPg4cDyAOGAmAGDNS6+6b8/HwVEU6nHARa2h7b4mt3nHSOHFELA78S2fBuV2/Ulz961IQjpp7r4l6548b6brbYsvnzxvpuPtN24dwr/d+RQWLzjIsS2v1Kq4p4dR+3KqzZGsOo6V/qHRwMvL2z7n5tXYODDku42+XaOOCXFbdjovD5J4FmRcNvMYdur7K/27sPiW1XjfYXsXHvfPvOfAwoFQfr18mlz+73m3rMY7Z0215tyyx79ZHg8iImoebffJkGuilKme1+cT4YqvK09GShjiK00uYb+r6ZWs5NaWW2pV3NPDqH051aYhzsmMXuMzyHKnVLIo23YbZ0HGZTOPYae+j0snsHHrEEzd/Th6zZHFf3Omus6PzfB4EBFR82i7T4ZcE6XsAxS367cNZSqSkMov50z1lSaXsw+8vJKV3NpyS62Ke3oYtS+n2jQVwRLfAi53SiWLsm23cRZkXDbzGHbq+67RHGZM7oIhux9Hrzmy+G/CENf5sRkeDyIiah5tdzA0bUIHri1Lg7q2KA3K7fq7Vr9WkYR01+rXSta9c9Wrrrctvnznqlcdb5//zdCyB190bMsrtSru6WHUvpxqsyMpjmNlSlc68PLytq8/Yy5m9HT5bqN3XNoxIW7qeOflQRLPgozLZh7DTn3fb8o4LFs0F/c/+9fC4/6jhzbgmk+8zTFNLv/32kVz8ej6LdacW/b4N8vjQUREzaMt0uTKU466OxIlKVblaVDlaVa949LoH8ogmzPRlU4gkzWR8UiLC3K5OMkunyY3nMkhWZQml82ZSDqkyZWLU3pYi2qqNLk4carNTCbnmOw2Opp1TJkLI03OLanObXk+Ta54DBqGBBpnTv0D4NhGq6TJpZIGkvb9Gcma6EwZGM5YCXLj0wmMZE3kTEWiOE3O/juxy8D2IZMJme2LaXK1CikwodwcOyzBSUmAQjmHAIVmCzdgmlxra/nfDI0loSmVSmCfyeNKlk23X3QVt+WUBlee+AYA+3Qmx9yX6Wn/uygu6WFE5cprM5s1HRPcDpk2AS/273IcIx0dSezTUTkenOq+szNZGHfFnMa2aSpe6NvpuM1k0sD0SV0V7QQZZ+X9qzYPNOsYzvfd6/6ZpuL5N7aX7Pd8mtySY2cX5s49One326yPBxERNYeGfE1ORD4mIs+IiCki8zzWe1lE1ojIEyIyprfOw0xoKm/LKQ2OiW9E1W3eMeKY1LZ5x0jdx0i9x2WrzwNe989pv+dT5LzmTiIioqg06pOhpwF8FMB1PtY9RlX7xrqhMBOayttySzti4huRt0zODJTgFuUYqfe4bPV5wOv+eaV1es2dREREUWnIJ0Oq+pyqrq3HtsJMaCpvyy3tiIlvRN5SCSNQgluUY6Te47LV5wGv++e23/NzqdvcSUREFJW4/2ZIAfxGRBTAdap6fdAG8ilH5d9fH0siUXlb+eSj8t8MVUt8C6MvRM1s2oQOLFs0t+I3Q9MmdNR9jNR7XLb6POB1/0xTK/Z7/jdDXnMnUbMKEsLQzLzCFVBDWMJZ9+5XcvmG418Zc1tEbiI7GBKR3wHYy+GqL6nqCp/NvEtVXxeRaQB+KyLPq+pDLts7B8A5ADBz5u5BaRiCQ/aciLvPn19zIpFTW5M6k7jj3KN9Jb6F2Rdqbm712i6SSQOH7jnRcezUe4zUe1w26zzgt2a97p9hSMl+TxgCQ4DLTjy8alomUVDtPs8SkT+RHQyp6vtDaON1++9mEbkbwFEAHA+G7E+NrgesCM3i68JMaHJqyyltKsjtqf141Wu7cEtqa8QYqfc2m3EeCFKzXvfPbb8ThY3zLBH5Edu34URkvIhMzJ8H8A+wgheIiIiIiIhq1qho7Y+IyEYARwNYKSL32cuni8i99mp7AvijiDwJ4P8ArFTVXzeiv0RERERE1HoaEqCgqncDuNth+SYAx9vnNwB4S527RkREREREbUJUW+9rtCKyBcArAHoBjPl/FIWgkdvnfQ9fn6oeF3ajRfVaq0bv87C0wv2Iy32Ie80GFZfH1Qv7WJtWqNk4P77l2NfaRVKzVD8teTCUJyKrVHVeO26f971x971RWuV+t8L9aIX7EEfN8Liyj9RMjy/7ShTjAAUiIiIiIqIo8WCIiIiIiIjaUqsfDF3fxtvnfW8/rXK/W+F+tMJ9iKNmeFzZR2qmx5d9pbbX0r8ZIiIiIiIictPqnwwRERERERE5avmDIRH5jog8LyJPicjdIjKpztv/mIg8IyKmiNQlBUVEjhORtSLygohcXI9tFm17uYhsFpGn67lde9v7isgDIvKs/Zj/S7370GiNrvdaNLJuw8IajF4j5lS/4l7DjZyf20mcazQv7rVajHVLUWv5gyEAvwVwuKoeAWAdgEvqvP2nAXwUwEP12JiIJAD8EMAHARwG4DQROawe27bdAKBReftZAF9Q1cMAvAPAZ+t83+Og0fU+JjGo27CwBqNX1znVryap4RvQuPm5ncSyRvOapFaL3QDWLUWo5Q+GVPU3qpq1L/4ZwIw6b/85VV1bx00eBeAFVd2gqqMAbgewsF4bV9WHAAzUa3tl2/6rqj5un98O4DkA+zSiL43S6HqvQUPrNiysweg1YE71K/Y13Mj5uZ3EuEbzYl+rxVi3FLWWPxgqczaAXzW6ExHbB8BrRZc3og1fjInI/gDeCuB/G9uThmqmem+5umUNtp2Wq2FqWaxVoiLJRncgDCLyOwB7OVz1JVVdYa/zJVhfYbm1Edun+hGRCQDuAnCBqr7Z6P6ErdH1TtW1eg1GjXMqxR1rlKh1tMTBkKq+3+t6ETkLwIcAHKsRZIlX236dvQ5g36LLM+xlbUFEUrBehN6qqj9vdH+i0Oh6j0jL1G071GDUYjan+tUyNUzVNWmN5rFWiYq0/NfkROQ4AF8EcKKq7mp0f+rgMQCzROQAEUkDOBXAPQ3uU12IiAD4CYDnVPWqRvenEZq43luiblmDba0lapjaAmuVqEjLHwwB+AGAiQB+KyJPiMiyem5cRD4iIhsBHA1gpYjcF+X27B/Pfw7AfbB+vH2Hqj4T5TaLichtAP4E4BAR2Sgin67XtgHMB3AGgPfZ+/oJETm+jtuPg4bW+1g1um5DxBqMWL3nVL+aoYYbPD+3jbjWaF4z1Gox1i1FTZrnWzREREREREThaYdPhoiIiIiIiCrwYIiIiIiIiNoSD4aIiIiIiKgt8WCIiIiIiIjaEg+GiIiIiIioLfFgqMmIyAIR+aXf5SFs78MicljR5QdFZJ6P2+0dRn9EZKqI/LrWdqjxxlqjIjJdRO50ua5QjyLyH0XL9xeRp322f4GIfDJovxza+ZyInF1rOxQdETlLRKb7WO8GETnZ7/IQ+sXaJU+11q6P2y12qqXiehSRI4v/VYCIXCYi/+qjbRGR+0Vkj6D9cmjrdyIyudZ2iIrxYIiq+TCAw6quVekiAD+qdeOqugXAX0Vkfq1tUXNS1U2q6ufJ/T+qr1JKRJIAzgbw08Adq7QcwOdDaIeicxaAqi8oG4C1S9WchQhrV1WXqepNVVY7EsBY/m/a8QCeVNU3x3DbcjcDOD+EdogKeDAUMhEZLyIrReRJEXlaRD5uL58rIn8QkdUicp+I7G0vf1BEvm//c8anReQoe/lRIvInEfmLiDwqIocE7MNyEfk/+/YL7eVnicjPReTXIrJeRL5ddJtPi8g6+zY/EpEfiMg7AZwI4Dt2/w6yV/+Yvd46EXm3SzdOAvBru+2EiPx/9v17SkQ+by9/WUS+abe9SkTeZj82L4rI4qK2fgHgE37vP41No2rX3uYR9vm/iMiX7fNfFZHPlL0z2SUit4vIcyJyN4Aue/mVALrsvtxqN52wa/kZEfmNiHQ5bP59AB63/wkhRORg+53HJ0XkcRE5SKxPtP4gIitEZIOIXCkin7DHwJr8uFDVXQBezj8OFC27Lp4XkVvterhTRMbZ11XUrFjvls8DcKtdJ10i8mURecyu3+tFRAJs32tcfKt8jhSRcSJyh4g8KyJ3i8j/isg81m77qXftisg0EVltn3+LiKiIzLQvv2jXZuFTHrsPT4rIkwA+ay9LA/gqgI/bffi43fxhds1vEJElLl34BIAVRf35pFivBZ4UkZvtZTeIyLUi8me7rQVivY55TkRuKGrrHgCnBXzIibypKk8hnmAdBPyo6HI3gBSARwFMtZd9HMBy+/yD+fUBvAfA0/b5PQAk7fPvB3CXfX4BgF86bLewHMA3ACyyz08CsA7AeFjvLG2w+9QJ4BUA+8J6t+llAD12Xx8G8AP79jcAOLloOw8C+K59/ngAv3PoywEAVhddPg/AnUX3p8f++zKA8+zzSwE8BWAigKkA3ii6/T4A1jR637b6qYG1ezGsJ9xuAI8BuM9e/gCAQwDsX9T2RUXbPwJAFsA8+/KOojb3t6870r58R35MlG37cgCfL7r8vwA+Yp/vBDDO7vc2AHsD6ADwOoDL7XX+BcD3im7/JQBfaPS+bIeTvY8VwHz78nIA/+qjZucVtdFTdP5mAP9kn78BRfNe0To3ADjZxzYq5ki7b9fZ5w9n7bbvqUG1+wysuflzsObZTwDYD8Cf7OsvA/Cv9vmnALzHPv8d7J5/z4L92qDoNo/atdULoB9AymHbrwCYaJ//e1ivSXqL74fd79sBCICFAN4EMAfWm/ar8+PBXnc9gCmN3o88tc4pCQrbGgDfFZFvwXrh97CIHA7rye+39ps3CQB/LbrNbQCgqg+JyB4iMgnWQcGNIjIL1qSZCtCHfwBwouz+Lm8ngJn2+d+r6iAAiMizsCbDXgB/UNUBe/nPAMz2aP/n9t/VsCb1cnsD2FJ0+f0Alqn9DmZ+O7Z77L9rAExQ1e0AtovIiIhMUtVtADYjnl9taTWNqt2HASwB8BKAlQA+YL9LeoCqrhWR/YvWfQ+Aq+1tPiUiT3m0+5KqPmGf96rV5wBARCYC2EdV77bbH7aXA8BjqvpX+/KLAH5j334NgGOK2tsM4NAq95fC85qqPmKfvwVWHf0a3jVb7BgR+SKsA4ceWC8Y/8fHdg+psg2nOfJdAL4PAKr6NGu37dW7dh8FMB/WHPoNAMfBOvB4uHglew6fpKoP2YtuBvBBj3ZXquoIgBER2QxgTwAby9bpsZ/bAesTzZ+pah9Q8Xrgf1RVRWQNrDdE19h9egbWGMiPifxrgn6PfhH5xoOhkKnqOhF5G6x3BK8Qkd8DuBvAM6p6tNvNHC5/DcADqvoR+8XggwG6IQBOUtW1JQtF3g5gpGhRDmOrgXwbbrcfgnUAFqQts6xvZlHbnXabFKEG1u5jsL4CsgHAb2EdnH8G1ovAWpTXutNXjfzWanltFtdt8RhgrdaXU/0JvGsWACAinQCugfVu+2sichn8z1vVtlFtjqyGtdv66l27DwF4N6w3QFcA+Hd7myuDd72En9cUWRExVNX02ZbX6wGAtUoh42+GQiZW2ssuVb0F1sfLbwOwFsBUETnaXiclIn9fdLP8bzPeBWDQ/uSmG9ZXGgDro+kg7gPw+fx3iEXkrVXWfwzAe0Vkslg/yj2p6LrtsN7pD2IdSt/J/C2Ac+22ISI9AdubDcBXuhKNXaNqV1VHAbwG4GMA/gTrncp/hfXkXe4hAKfb2zwc1lfl8jIiEuQTVMB6Z/1gux/bAWwUkQ/b7Xfkv8cfAGu1vmbmaxNWXfwR3jVbPJ/lXzz2icgEWF9/86vauHDyCIBT7PUPg/UVoDzWbvupd+0+DGARgPX2QckArDe+/li8kv1tjG32nA6U/l53LK8HAOt+HWifvx/W746nAMFfD9iva/aC9TV7olDwYCh8cwD8n4g8AeArAK6wX+ydDOBb9g8SnwDwzqLbDIvIXwAsA/Bpe9m3AXzTXh70ncWvwfpq0lP2x8tf81pZVV+H9bH5/8F6wn4ZwKB99e0A/k2sH7Yf5NxCRXs7AbwoIgfbi34M4FW7P0/CfjEbwDGo/d0rqq6RtfswgM2qOmSfn4Gyr2/YrgUwQUSeg/Vj3uJPj66HVWO3OtzOza9gfW0k7wwAS+yvMD0K60k3iPmwDv6pPtYC+KxdD5MBXFulZm8AsMyu8RFYiZdPw3oD6TG/G/UxLpxcA+uF7rMAroD1tab8PMvabT91rV1VfRnWJ0/5N5n+CGCbqm51WP1TAH5ob6s4mOEBWIEJxQEKfqyE9fs1qOozAL4O4A/2fbwqQDsAMBfAn/NfuycKg6iWf1JL9SQiD8L60eKqBvdjgqrusD+9uRvWjzbvrqG9jwCYq6qXhtC3hwAsdJm0qUHiUru1EiuV7ouqur7Gdt4K4CJVPSOcnpEX+yuYv1TVwxvcFV9EJAHrx+XD9htLvwNwiP0CeKxtsnabULPVbq3ESlq8SVU/EEJb3wdwj6r+vvaeEVn4myHKu0xE3g/r4/ffwIqzHjNVvTv/MXgtRGQqgKt4IEQRuhjWj9FrekEJ67dO/2/t3aEWNQ7AA/bX4QTA+bUcCNlYuxR7qvpXsaLi99Da/9fQ0zwQorDxkyEiIiIiImpL/M0QERERERG1JR4MERERERFRW+LBEBERERERtSUeDBERERERUVviwRAREREREbUlHgwREREREVFb+v8BvLM5AHAZk0wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 823.25x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.pairplot(df, hue='species', diag_kind='hist');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use numerical gradients in CEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do not have access to the Keras or TensorFlow model weights, we can use numerical gradients for the first term in the loss function that needs to be minimized (eq. 1 and 4 in the [paper](https://arxiv.org/pdf/1802.07623.pdf))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CEM parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'PN'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If numerical gradients are used to compute:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial L}{\\partial x} = \\frac{\\partial L}{\\partial p} \\ast \\frac{\\partial p}{\\partial x}\n",
    "$$\n",
    "\n",
    "with L = loss function; p = predict function and x the parameter to optimize, then the tuple *eps* can be used to define the perturbation used to compute the derivatives. *eps[0]* is used to calculate the first partial derivative term and *eps[1]* is used for the second term. *eps[0]* and *eps[1]* can be a combination of float values or numpy arrays. For *eps[0]*, the array dimension should be *(1 x nb of prediction categories)* and for *eps[1]* it should be *(1 x nb of features)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps0 = np.array([[1e-2, 1e-2, 1e-2]])  # 3 prediction categories, equivalent to 1e-2\n",
    "eps1 = np.array([[1e-2, 1e-2, 1e-2, 1e-2]])  # 4 features, also equivalent to 1e-2\n",
    "eps = (eps0, eps1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For complex models with a high number of parameters and a high dimensional feature space (e.g. Inception on ImageNet), evaluating numerical gradients can be expensive as they involve multiple prediction calls for each perturbed instance. The *update_num_grad* parameter allows you to set a batch size on which to evaluate the numerical gradients, drastically reducing the number of prediction calls required.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "update_num_grad = 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate pertinent negative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "lr = load_model('iris_lr.h5')\n",
    "predict_fn = lambda x: lr.predict(x)  # only pass the predict fn which takes numpy arrays to CEM\n",
    "                                      # explainer can no longer minimize wrt model weights\n",
    "\n",
    "# initialize CEM explainer and explain instance\n",
    "cem = CEM(predict_fn, mode, shape, kappa=kappa, beta=beta, \n",
    "          feature_range=feature_range, max_iterations=max_iterations, \n",
    "          eps=eps, c_init=c_init, c_steps=c_steps, learning_rate_init=lr_init, \n",
    "          clip=clip, update_num_grad=update_num_grad)\n",
    "cem.fit(x_train, no_info_type='median')\n",
    "explanation = cem.explain(X, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original instance: [[ 0.55333328 -1.28296331  0.70592084  0.92230284]]\n",
      "Predicted class: virginica\n"
     ]
    }
   ],
   "source": [
    "print(f'Original instance: {explanation.X}')\n",
    "print(f'Predicted class: {class_names[explanation.X_pred]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pertinent negative: [[ 0.55333328 -1.28296331  0.70592084  0.92230284]]\n",
      "Predicted class: virginica\n"
     ]
    }
   ],
   "source": [
    "print(f'Pertinent negative: {explanation.X}')\n",
    "print(f'Predicted class: {class_names[explanation.X_pred]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.remove('iris_lr.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
